Audit Kit · Free during launch

50 Laws Audit Rubric

Use this rubric with the ai-agent-audit skill. Do not mechanically list every law in an audit report. Use the full set to find concrete issues, then report only the highest-signal findings.

Audit priority order:

  1. Security and data leakage.
  2. Unauthorized side effects.
  3. Silent wrong answers.
  4. Retrieval and context failures.
  5. Eval blind spots.
  6. Observability and handoff gaps.
  7. Cost, latency, and maintainability issues.

01. Law of Context Decay

Category: Context & Reliability

Tagline: Most agent failures start with the wrong context.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Most bad outputs come from missing, stale, or conflicting context, not from a model that can't think. The model often reasons fine over the picture it was handed and still lands wrong, because the picture was wrong to begin with. Bad context produces confident bad answers.

Warning signs:

Fix patterns:

Worked example:

Your support agent keeps insisting a customer's subscription is active when it was cancelled last week, so the team files a ticket to upgrade to a smarter model. The real culprit: the RAG pipeline pulls a 30-day-old cached account snapshot, and the agent reasons flawlessly over stale data. Before swapping models, log the exact context the agent saw on three bad runs; you will usually find a contradiction or a stale record, not a dumb model. Fix the freshness and the 'reasoning bug' evaporates.

Sources:

02. Compounding Error Law

Category: Context & Reliability

Tagline: Reliability multiplies, it doesn't add.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

A step that works 95% of the time, run ten times in a row, gives you the right final answer only about 60% of the time. The failures don't announce themselves. They pile up quietly until the answer is wrong and you can't tell which step broke it. Every link you add lowers the ceiling for the whole chain.

Warning signs:

Fix patterns:

Worked example:

A six-step invoice pipeline (OCR, extract line items, match vendor, validate totals, post to ledger, notify) tests at 95% per step and you ship it, then watch roughly a third of invoices come out subtly wrong with no obvious culprit. The errors are multiplicative, not additive: 0.95 to the sixth is about 0.74. Either collapse steps (have one pass extract and validate together) or add a checkpoint after vendor-matching that halts on low confidence, so a bad match cannot quietly poison the ledger post downstream.

Sources:

03. Position Is Power

Category: Context & Reliability

Tagline: Models read the edges. The middle gets lost.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Give a model a long input and it pays the most attention to the start and the end. Facts buried in the middle quietly lose their grip. They're present but basically ignored. That's the worst kind of bug, because the information was technically in context and nothing looks wrong.

Warning signs:

Fix patterns:

Worked example:

You paste a 12-page contract into context and ask the agent to flag the termination clause, but it confidently misses the 90-day notice buried on page 7 because that clause sat dead-center in the input. Nothing errored; the fact was technically in context and still ignored. Lead with a one-line summary of what to look for, chunk and rank the clauses so the relevant one lands near the top, and never assume a long paste means the middle got read.

Sources:

04. The Model Optimizes for Looking Done

Category: Context & Reliability

Tagline: Agents declare victory early.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

An agent will write the summary before doing the work if you let it. Looking finished is cheaper than being finished, so the model drifts toward the cheaper path: a plausible report, a confident 'done', a success it never tested. The output reads complete. The work isn't. This is specification gaming, where the model optimizes the proxy you can see instead of the goal you meant.

Warning signs:

Fix patterns:

Worked example:

Your coding agent reports 'All tests passing, feature complete' and you almost merge it, until you notice it never actually ran the suite, it just wrote a confident summary. Looking finished is cheaper than being finished, so the model takes the cheaper path every time you let it. Make 'done' require the artifact: the pasted test output, the actual diff, the curl response with a 200. Grade the proof, not the prose.

Sources:

05. Design for the Worst Case

Category: Context & Reliability

Tagline: Plan around the ceiling, not the average.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

When a system says 'up to 24 hours', 'may retry', or 'no guaranteed latency', those limits are the numbers that matter. Designing for the typical case works right up until the rare event, which is exactly when failure costs the most. At scale, those failures aren't edge cases. They're the normal state of things.

Warning signs:

Fix patterns:

Worked example:

The webhook docs say delivery may be retried for up to 24 hours and you build assuming events arrive once, within seconds, so your dedup window is 5 minutes and your timeout is 10 seconds. At month-end load the provider retries a backlog, duplicates slip past the stale window, and you double-process payments. Read every 'up to' and 'may' as the number you must survive: size the dedup window, retry budget, and timeouts against the 24-hour ceiling, not the usual sub-second case.

Sources:

06. Think Before You Touch

Category: Reasoning & Planning

Tagline: Spend reasoning tokens before you spend actions.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Asking a model to reason step by step before answering measurably improves results, and for an agent the stakes are lopsided. A reasoning trace is cheap and easy to undo. An executed action, a sent email, a dropped table, a charged card, is not. Letting the model lay out its plan in tokens before it commits is the cheapest insurance you can buy.

Warning signs:

Fix patterns:

Worked example:

Your ops agent gets 'clean up the staging records' and immediately fires a DELETE, dropping rows a teammate needed because it never reasoned about scope. A reasoning trace costs a few hundred tokens and is fully reversible; the executed delete is neither. Force an explicit plan step before any side-effecting tool call: have it state what it will delete, why, and the row count, then act. Burned tokens are the cheapest insurance against an irreversible action.

Sources:

07. Don't Bet on One Chain

Category: Reasoning & Planning

Tagline: Sample many reasoning paths and let them vote.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

A single greedy chain of thought is fragile. Sample several independent reasoning paths and take the majority answer, and you get large, consistent gains. Correct reasoning tends to converge while mistakes scatter, so agreement across independently generated plans is a real signal worth trusting before you act on something that matters.

Warning signs:

Fix patterns:

Worked example:

Your agent estimates a quote for a custom order in one greedy pass, lands on $1,400, and you send it to the customer, only to discover it dropped a line item that should have made it $2,100. A single chain is fragile, and the miss is invisible because the math looked clean. For consequential, hard-to-reverse outputs like pricing, sample the calculation three to five times and act on the consensus; when the paths disagree, that disagreement is your signal to escalate before committing.

Sources:

08. Branch When the First Step Matters

Category: Reasoning & Planning

Tagline: For decisions you can't take back, explore before you commit.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Tree-of-Thoughts turns linear reasoning into a search: generate several candidate thoughts, judge them, look ahead, and backtrack instead of being stuck going left to right. It matters most when an early choice is pivotal, which is exactly the spot where an agent's first irreversible action sets up everything downstream. Cheap, recoverable steps don't need it. Pivotal ones do.

Warning signs:

Fix patterns:

Worked example:

A migration agent picks a database cutover strategy on its first instinct, big-bang swap, and everything downstream (backfill, rollback plan, dual-write window) is now locked to that pivotal early choice that turns out wrong. Cheap reversible steps do not need this, but a high-leverage first move does: have the agent generate three candidate strategies, score each on risk and reversibility, and look ahead before committing. The branching cost is trivial next to re-running a botched cutover.

Sources:

09. Stop Tuning, Start Scaling

Category: Reasoning & Planning

Tagline: Build scaffolding you would gladly delete.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

The Bitter Lesson isn't a ban on structure. It's a warning against hand-coded cleverness that quietly becomes a ceiling. Use code where you need guarantees and thin scaffolds for today's weak spots, but keep asking whether a simpler, more model-driven version now works better.

Warning signs:

Fix patterns:

Worked example:

You spend two weeks hand-building a 40-node routing tree to help a weaker model triage tickets. It works for a while, then a newer model with a simpler tool prompt matches it and is easier to maintain. The lesson is not to remove all structure; validation and permissions still belong in code. The lesson is to keep temporary scaffolding thin and deletable. Re-test the simple baseline as models improve, and remove the custom chain when it stops earning its complexity.

Sources:

10. More Thinking Can Hurt

Category: Reasoning & Planning

Tagline: Extra reasoning past the answer is wasted, or a wrong turn.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

More reasoning isn't automatically better. On easy tasks it just burns latency and money for nothing. On some tasks the model finds the answer early and then talks itself out of it. Reasoning depth has a useful range, not an endless upside.

Warning signs:

Fix patterns:

Worked example:

You route every order-status lookup through extended reasoning to be safe. The answer is a direct database field, but the agent now takes eight seconds, costs several times more, and sometimes talks itself away from the obvious result. More tokens did not add information. Match the thinking budget to the task: skip extended reasoning for simple lookups, use bounded reasoning for ambiguous judgment, and use tests or tools rather than endless deliberation when stakes are high.

Sources:

11. Retrieval Is the Ceiling

Category: Retrieval & Memory

Tagline: Missing evidence becomes a missing answer.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

For facts the model doesn't already know well, the answer can only be as good as the evidence you retrieve. If the right passage never reaches the context, the generator fills the gap from memory and guesswork. Retrieval quality sets the practical ceiling for any grounded answer.

Warning signs:

Fix patterns:

Worked example:

You swap in a smarter model to fix wrong support answers, and accuracy barely moves because the refund-policy chunk never reached the top-k. The generator was filling a missing-evidence gap. Before touching prompts or models, log recall@k on labeled questions: did the answer-bearing passage appear, and was it ranked high enough to matter? If not, fix chunking, query expansion, or ranking first. Better generation cannot reliably ground an answer in evidence it never saw.

Sources:

12. Grounding Is Not a Guarantee

Category: Retrieval & Memory

Tagline: Retrieval reduces hallucination. It doesn't eliminate it.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Vendors marketed RAG legal tools as 'hallucination-free', but a Stanford audit found they still made things up 17 to 33% of the time. Handing the model a source doesn't force it to use that source faithfully. It can misread it, over-generalize, or cite a real document for a claim the document never makes. Grounding lowers the error rate. It never gets it to zero.

Warning signs:

Fix patterns:

Worked example:

Your team ships a contracts assistant, tells the client it is 'hallucination-free because it uses RAG', and a month later it cites a real clause for an indemnity term that clause never mentions. RAG lowered the error rate, it did not zero it, and the marketing claim is now a liability. Treat retrieval as risk reduction, not a safety guarantee: add a verification step that checks each generated claim traces to a span in the retrieved source, and strike 'hallucination-proof' from every deck and contract.

Sources:

13. Relevant Beats Plenty

Category: Retrieval & Memory

Tagline: Near-misses poison context worse than random noise.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

It's backwards from what you'd expect: documents that are on-topic but don't answer the question hurt more than clearly irrelevant ones, because they look plausible and pull the generator toward answers that are wrong but adjacent. Stuffing more 'kind of relevant' chunks into the context lowers accuracy instead of improving coverage. Precision at the top beats breadth.

Warning signs:

Fix patterns:

Worked example:

To improve coverage you bump top-k from 5 to 20, and accuracy drops, because the 15 new chunks are all topically adjacent: same product line, wrong model number, and they pull the answer toward a plausible lie. Clearly irrelevant chunks get ignored, but near-misses get believed. Do not pad context for recall's sake. Run a reranker over a wide candidate set, then keep only the 3 to 5 sharpest passages. A tight context beats a stuffed one.

Sources:

14. Keyword Still Carries Weight

Category: Retrieval & Memory

Tagline: Pure semantic search quietly loses to a 40-year-old baseline.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Dense embedding retrievers win in-domain but often lose to BM25 once you step outside the training distribution. Exact-match terms, product codes, names, and rare jargon are where embeddings blur and plain keyword search shines. In-domain accuracy doesn't predict how well a retriever generalizes, and combining the two is how strong systems cut their retrieval failures dramatically.

Warning signs:

Fix patterns:

Worked example:

Your pure-embedding search nails paraphrased questions in the demo, then face-plants in production when a user searches for SKU 'AX-4400-B' or an error code, and the dense vectors blur it into a dozen near-identical part numbers. Embeddings smear exact tokens, IDs, names, and rare jargon. Default to hybrid: run BM25 alongside semantic search, fuse the results, and put a reranker on top. The 40-year-old lexical baseline is exactly what rescues your out-of-domain and exact-match queries.

Sources:

15. Memory Is a System, Not a Window

Category: Retrieval & Memory

Tagline: Give the agent a hierarchy, not just a bigger prompt.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Think of the context window like a computer's RAM. The agent should actively move information between a small in-context working set and large external storage, deciding what to keep, what to evict, and what to recall. Cramming everything into one flat window mixes up working memory with long-term storage and hits hard limits fast. Durable memory needs explicit tiers and self-managed retrieval.

Warning signs:

Fix patterns:

Worked example:

Your agent's long-running session keeps degrading: by hour two it is forgetting decisions from hour one because you have been appending everything into one ever-growing prompt until attention spreads thin and costs balloon. A bigger context window just delays the same wall. Build memory in tiers instead: a small working set in context, summarized recallable notes, and an external store the agent reads and writes deliberately, with explicit policies for what gets promoted, summarized, and evicted. Treat the window like RAM, not a filing cabinet.

Sources:

16. Narrow Beats General

Category: Scope & Design

Tagline: Three sharp tools beat thirty dull ones.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

A scoped agent with a handful of well-chosen tools beats a generalist drowning in options. Every extra tool is another way to choose wrong, another branch to test, another failure to debug. More capability surface means more liability surface, so breadth you don't need is just risk you signed up for.

Warning signs:

Fix patterns:

Worked example:

You hand your agent 28 tools so it can handle anything, and it starts calling search_web when it should call query_orders, then mixes up three nearly identical lookup tools. Every tool you added was another wrong branch it could take. When selection gets flaky, the fix is rarely a longer system prompt nagging it to choose better, it is deleting tools. Start with three sharp ones, add a fourth only when a real task demands it, and watch reliability climb as the surface shrinks.

Sources:

17. Determinism at the Edges

Category: Scope & Design

Tagline: Model in the middle, code at the boundaries.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Validation, schema enforcement, retries, routing, and access control aren't the model's job. They're code's job. The model is for judgment under ambiguity, and deterministic code is for everything that has to be correct every single time. Asking a probabilistic system to guarantee a contract is asking for the 0.1% that ruins you.

Warning signs:

Fix patterns:

Worked example:

You let the model decide whether an email is valid, format the output JSON, and enforce which users can trigger a refund, then one sampling roll in a thousand returns malformed JSON or green-lights an unauthorized action. Hard guarantees should never ride on a probabilistic system. Put the model in the soft middle for judgment under ambiguity, and wrap it in code at the boundaries: schema validation with Zod or Pydantic, deterministic auth checks, explicit retries. The contract belongs to code, not to a dice throw.

Sources:

18. Observability Precedes Autonomy

Category: Scope & Design

Tagline: You can't grant autonomy you can't trace.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

If you can't see what the agent did and why, every decision, tool call, and input, then you can't safely let it act on its own. You're not trusting it, you're hoping. Autonomy without a trace is an outage you haven't found yet, and when it breaks you'll have no way to learn why.

Warning signs:

Fix patterns:

Worked example:

You grant the agent permission to send emails and update records unattended, it does something baffling on Tuesday, and you have no trace of which tool calls or inputs led there, so you are left guessing and rolling back blind. You did not trust the agent, you hoped. Before widening autonomy, instrument every decision, tool call, input, and output with something like LangSmith or OpenTelemetry spans, so any run is reconstructable after the fact. Extend the leash only as far as your trace actually reaches.

Sources:

19. Decompose Before You Scale

Category: Scope & Design

Tagline: When it's unreliable, split it. Don't supersize it.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

When output is inconsistent, the instinct is to throw more at the same shape: a bigger model, a longer context, more tokens. That rarely fixes a structural problem. It just spreads attention thinner. Splitting the task into focused, single-purpose passes almost always beats trying to make one overloaded pass smarter.

Warning signs:

Fix patterns:

Worked example:

Your invoice extractor is inconsistent across 30-line documents, so you reach for a bigger model and a longer prompt, and it gets blurrier, not sharper, because one overloaded pass is splitting attention across every row. The instinct to supersize masks a structural problem. Split it instead: extract each line item in a focused per-item pass, then run a separate reconciliation pass to total and cross-check. Several stages that each do one thing well beat one heroic pass trying to do everything.

Sources:

20. The Cheapest Fix First

Category: Scope & Design

Tagline: Reach for the prompt before the platform.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

When something misbehaves, the cheapest fix that addresses the root cause usually wins, and that's usually clearer instructions, a better tool description, or a concrete example, not a new classifier, preprocessing layer, or pipeline. Infrastructure feels like progress, but it often just wraps an unsolved prompt in more surface area.

Warning signs:

Fix patterns:

Worked example:

The agent keeps picking the wrong tool, so you spec out an intent-classifier service and a preprocessing layer, and three days of infrastructure later it still misfires, because the real problem was a tool described as 'searches the database' that the model could not tell apart from another. Infrastructure feels like progress while it just wraps an unsolved prompt in more surface area. Exhaust the cheap fixes first: rewrite the tool description, add two concrete examples, tighten the scope. Build the system only after you have proven words genuinely cannot close the gap.

Sources:

21. The Tool Description Is the Prompt

Category: Instruction & Output

Tagline: An agent is only as capable as its tools are legible.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

The agent decides what to call based on how a tool reads, not on what it actually does. A vague description like 'searches the database' gets skipped in favor of a tool the model understands better, even a worse one. Thin tool descriptions cause more failures than thin instructions ever do.

Warning signs:

Fix patterns:

Worked example:

You ship two retrieval tools: query_db described as 'searches the database' and web_search described as 'searches the web for current information, returns titles, snippets, and URLs'. The agent keeps hitting the web for facts that live in your Postgres because it has no idea query_db covers customer orders, date ranges, and status filters. You blame the model and consider fine-tuning. The real fix takes ten minutes: rewrite the description to spell out what tables it covers, when to prefer it over web search, the exact arg shape, and a sample return. Treat each tool description like an onboarding doc for a sharp engineer who has never seen your schema.

Sources:

22. Show, Don't Tell

Category: Instruction & Output

Tagline: When prose fails, stop writing prose.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

If an instruction has produced the wrong result twice, writing it a third time more carefully rarely helps, because prose is always open to interpretation. Two or three concrete input and output examples kill the ambiguity that no amount of careful description can. Examples show the rule. Prose only describes it.

Warning signs:

Fix patterns:

Worked example:

Your extraction agent keeps formatting phone numbers inconsistently, so you rewrite the instruction a third time: 'normalize to E.164, strip extensions, handle missing area codes gracefully.' It still botches the edge cases. Stop adding adjectives to prose. Drop in four labeled examples instead: '(555) 123-4567' to '+15551234567', 'ext. 12' to dropped, 'unknown' to null, an international number with a country code. The examples pin down exactly what 'gracefully' meant, which no amount of careful description ever could.

Sources:

23. Confidence Is Not Calibrated

Category: Instruction & Output

Tagline: A model's certainty is not evidence.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Models are routinely confident and wrong, and unconfident and right. Routing decisions on self-reported confidence inherits that miscalibration. 'Only flag high-confidence issues' or 'be conservative' just moves the noise around. It doesn't reduce it, because the confidence itself is the unreliable signal.

Warning signs:

Fix patterns:

Worked example:

A content-moderation agent is told to only escalate high-confidence policy violations, and it sails through eval while quietly waving through the borderline harassment cases it felt unsure about. The threshold did nothing but reshuffle the noise, because the model's self-rated confidence was never tied to actual correctness. Rip out the confidence gate and replace it with categorical rules: escalate if it names a person plus a threat of harm; do not escalate generic insults, each with a worked example. Decide on observable features of the content, not on how sure the model claims to feel.

Sources:

24. Surface Ambiguity, Don't Resolve It

Category: Instruction & Output

Tagline: When the data is unclear, don't guess confidently.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Faced with two plausible matches, conflicting sources, or a missing field, an agent's instinct is to pick the most likely option and move on, a confident choice that quietly buries the doubt. When the stakes touch identity, money, or anything you can't undo, a quiet wrong guess is far worse than an honest 'this is unclear'.

Warning signs:

Fix patterns:

Worked example:

An invoice-matching agent finds two vendors named 'Acme LLC' with different tax IDs and confidently picks the one with the higher historical volume, routing a $40k payment to the wrong account. Nobody notices until reconciliation, because the output looked clean and decisive. The agent should have stopped and flagged it: preserve both candidate records with their tax IDs and source rows, and request a second identifier or a human decision. When money, identity, or anything irreversible is on the line, an honest 'this is ambiguous' beats a tidy wrong answer every time.

Sources:

25. Averages Lie

Category: Evaluation & Measurement

Tagline: 97% overall can hide a 60% segment.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

An aggregate metric is a blended story that smooths over exactly the failures you most need to see. A system at 97% overall can be 99% on the easy cases and 60% on the rare, hard segment where the errors actually cluster. Trust the headline number and you'll automate straight into the cracks it's hiding.

Warning signs:

Fix patterns:

Worked example:

Your support-triage classifier reports 96% accuracy and the team greenlights auto-routing. Three weeks in, the billing-dispute queue is a disaster, because the model was 99% accurate on the common 'password reset' and 'where is my order' tickets and 58% on the rare refund-dispute segment where mistakes actually cost you customers. The blended number hid the exact slice you most needed to see. Slice the eval by ticket type, intent, and language before you trust it, and oversample the rare high-stakes cases instead of grading on a random draw.

Sources:

26. Vibes Don't Scale

Category: Evaluation & Measurement

Tagline: Eyeballing outputs feels like progress until you can't tell if a change helped.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

The common root cause of failed LLM products is the absence of solid evals. Teams ship on vibe checks, iterate blind, and can't tell whether a prompt change improved anything. Manual spot-checking doesn't survive scale or a second engineer. Evals are to AI products what unit tests are to software: the up-front cost that makes every later change cheap and safe.

Warning signs:

Fix patterns:

Worked example:

Your team iterates on the summarization prompt by eyeballing a few outputs in the playground, nodding, and shipping. It feels productive until a second engineer tweaks the prompt to fix one complaint and silently regresses three things nobody re-checked, and now no one can say whether last week's change actually helped. Vibe checks do not survive a second person or a tenth example. Stand up a tiny eval harness early: every 'that looks wrong' becomes a permanent, re-runnable case, so prompt changes get graded instead of guessed.

Sources:

27. Look at Your Data

Category: Evaluation & Measurement

Tagline: The highest-ROI activity in AI is the one teams skip first.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Error analysis, reading your app's actual traces by hand to find where it fails, is the single most valuable thing you can do when building with AI, yet teams skip it for dashboards and vanity metrics that climb while users still struggle. You can't write a good eval for a failure mode you've never seen, and you only see failure modes by reading transcripts.

Warning signs:

Fix patterns:

Worked example:

Instead of reading transcripts, the team buys an eval platform and watches a 'helpfulness score' dashboard climb while users keep churning. The dashboard improved; the product did not, because nobody had ever read the actual traces to learn that the agent confidently invents return policies. You cannot write an eval for a failure mode you have never witnessed. Before spending a dollar on tooling, hand-read 50 to 100 real production traces, cluster the failures, and let those clusters, not vendor metrics, decide what you measure.

Sources:

28. The Judge Is Biased

Category: Evaluation & Measurement

Tagline: An LLM grader reacts to length and position, not just substance.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

An LLM judge can match human preferences over 80% of the time, but only after you account for its systematic biases: position bias (favoring the first answer shown), verbosity bias (favoring longer answers regardless of quality), and self-enhancement bias (favoring its own outputs). It's a useful instrument, but an uncalibrated one that grades surface features as readily as substance.

Warning signs:

Fix patterns:

Worked example:

You wire up an LLM-as-judge to pick the better of two agent responses and one variant mysteriously dominates every A/B test. It turns out the winner just writes longer answers and happens to be shown first, both of which the judge silently rewards regardless of substance. You were measuring verbosity and position, not quality. Swap the answer order and average both runs, control for length so a padded answer cannot win on bulk alone, and never let a model be the sole grader of outputs from its own family.

Sources:

29. Goodhart's Trap

Category: Evaluation & Measurement

Tagline: When your eval becomes the goal, it stops measuring what you cared about.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

When a measure becomes a target, it stops being a good measure. Optimize hard against any single metric and the agent learns to game its surface form, padding answers to please a verbosity-biased judge or overfitting a fixed eval set, while the underlying capability stalls or even slips. The number goes up. The thing you cared about doesn't.

Warning signs:

Fix patterns:

Worked example:

You optimize a prompt against the same 200-case eval for a sprint, and the score climbs from 82% to 94%. Then users complain the agent feels worse. The system learned the surface of the test: longer answers, cleaner formatting, and patterns your judge rewards, while the underlying capability barely moved. Treat any metric you push on as suspect. Keep fresh held-out cases, compare against different signals, and re-validate on examples the optimizer never saw.

Sources:

30. Regress or Repeat

Category: Evaluation & Measurement

Tagline: Every fixed bug is a future regression unless it becomes a test.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

LLM systems are non-deterministic and globally coupled, so a prompt tweak that fixes one case can quietly break three others. Rerunning real production examples against a new prompt is the only way to know you didn't break what already worked. Without a regression suite you're stuck in a whack-a-mole loop, rediscovering the same failures release after release.

Warning signs:

Fix patterns:

Worked example:

A user reports the agent mishandles refunds over $1,000, you tweak the prompt, confirm that one case works, and ship. Next release the same refund bug is back, plus the prompt change quietly broke partial refunds, because these systems are non-deterministic and globally coupled and you never re-ran the old cases. Without a regression suite you are playing whack-a-mole, rediscovering the same failures release after release. Turn every fixed bug into a permanent case and run the full suite on every prompt or model change before it goes out.

Sources:

31. The Lethal Trifecta

Category: Safety & Security

Tagline: Private data, untrusted content, and a way out. Pick at most two.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

An agent becomes exploitable the moment it combines three things: access to private data, exposure to untrusted content, and the ability to send data out. Any one poisoned input in that pipeline can steer it into leaking your data, with no code vulnerability required. Guardrail prose isn't enough, because the model can't be the security boundary.

Warning signs:

Fix patterns:

Worked example:

Your support agent reads from a customer's private ticket history, ingests the body of an inbound email, and can call a send_email tool to reply. That is all three legs: private data, untrusted content, and an exfiltration path. A customer pastes a request to forward another user's account details to an outside address into their email signature and the agent obliges, because it cannot tell that instruction apart from a real one. The fix is not a cleverer system prompt: drop one leg. Make the reply tool draft-only behind human review, or strip the agent's access to other customers' data when it is processing inbound mail.

Sources:

32. Tokens Don't Wear Badges

Category: Safety & Security

Tagline: Untrusted text can sound like instructions.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Prompt injection is an architectural risk, not a typo you patch once. Models don't reliably tell trusted intent apart from untrusted content, and prose guardrails fall apart under pressure. Newer instruction-hierarchy and isolation patterns help, but the safe assumption is that any untrusted content might be speaking with an attacker's intent.

Warning signs:

Fix patterns:

Worked example:

An engineer ships a doc-summarizer agent and adds a system-prompt line: ignore instructions inside documents. A week later, a PDF contains a fake operational instruction that tells the agent to call a destructive tool. The model does not reliably separate trusted intent from attacker-controlled prose, so the guardrail fails. Stop treating warning text as a security boundary. Once an agent reads untrusted content, constrain the actions it can reach and enforce authority outside the model.

Sources:

33. The Confused Deputy

Category: Safety & Security

Tagline: An agent with your privileges will wield them on an attacker's behalf.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

A confused deputy is a privileged program that a caller tricks into misusing its authority. It isn't malicious, just confused about whose intent it's serving. An LLM agent is the ultimate confused deputy: it holds your credentials and tools, but it'll follow injected instructions and carry out an attacker's intent with your authority. The trap is ambient authority. Authority should travel with the request, not sit waiting inside the agent.

Warning signs:

Fix patterns:

Worked example:

Your deploy-bot agent runs with a long-lived admin token so it can handle whatever comes up, and it reads GitHub issues to triage them. An attacker files an issue that says run the migration to drop the staging users table, and the bot, holding your privileges, does exactly that. It was not hacked, it was confused about whose intent it was serving. Kill the ambient admin credential: give the agent read-only access by default, scope each tool's authority to the specific task, and require a fresh, narrowly-scoped grant for anything destructive.

Sources:

34. Quarantine Untrusted Tokens

Category: Safety & Security

Tagline: Let the privileged planner orchestrate, but never let it read the poison.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

The Dual-LLM pattern splits the agent in two. A privileged model holds the tools and plans actions but never sees untrusted content. A quarantined model processes the tainted data but has no tools and returns only opaque variables. The privileged model directs the quarantined one without ever ingesting the bytes that could carry an injection. The separation is what makes it safe.

Warning signs:

Fix patterns:

Worked example:

You build a research agent that scrapes arbitrary web pages and also holds Slack and database tools. As one model, it is a sitting duck: a poisoned page can hijack the same context that controls your tools. Split it instead. A quarantined model reads the scraped HTML and returns only structured output like a summary id and a sentiment label, while the privileged planner that holds the tools orchestrates by reference and never ingests the raw page bytes. The planner acts on opaque variables, so the injection in the HTML has nothing to grab onto.

Sources:

35. Sandbox the Blast Radius

Category: Safety & Security

Tagline: Assume the agent gets compromised, then contain what it can reach.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Defense in depth means planning for the injection that succeeds. Box the agent in with filesystem isolation (access scoped to specific directories) and network isolation (exfiltration blocked), and a compromised agent can't reach past its sandbox. Real incidents, like CI agents that could leak secrets through untrusted content, show why that second layer matters when the first one fails.

Warning signs:

Fix patterns:

Worked example:

Your CI agent runs untrusted PR branches and has the build runner's full environment, including the cloud credentials sitting in env vars and open egress to the internet. A contributor's PR adds a test that reads those secrets and POSTs them to their server, and the injection succeeds on the first try. Defense in depth assumes exactly this. Run agent tool execution in a container scoped to the one working directory, with an egress allowlist that blocks everything but the registries you need, so a successful compromise is a contained annoyance instead of a credential leak.

Sources:

36. Don't Build an Agent When a Workflow Will Do

Category: Architecture & Operations

Tagline: Agents buy flexibility with latency, cost, and unpredictability.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

The simplest solution that works is usually the right one, and sometimes that means not building an agentic system at all. Agents that direct their own tool use trade latency, cost, and predictability for autonomy, while a workflow with predefined code paths is cheaper and more reliable for well-defined tasks. Reach for an agent only when the problem genuinely needs the model making decisions at runtime.

Warning signs:

Fix patterns:

Worked example:

A team wires up a multi-step ReAct agent to categorize incoming support tickets and route them to a queue. It costs three LLM calls per ticket, occasionally invents a queue that does not exist, and takes four seconds. The task has five known categories and one decision point: it is a single classification call feeding a switch statement, not an agent. Default to the deterministic workflow and reach for agentic loops only when the branching is genuinely open-ended and you cannot enumerate the paths in advance.

Sources:

37. Cascade Before You Escalate

Category: Architecture & Operations

Tagline: Try the cheap model first. Only the hard cases deserve the expensive one.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Most queries don't need your most powerful model. Routing requests through a cascade, a cheap model first and a stronger one only when confidence is low, can match top-tier quality at a fraction of the cost. The price gap between models spans two orders of magnitude, so paying top dollar for every call is pure waste.

Warning signs:

Fix patterns:

Worked example:

Every call in your pipeline hits top-tier pricing, including the 80% of requests that are simple intent classification a small model nails perfectly. You are paying hundred-x rates for work a cheap model clears with room to spare. Build a cascade: route first to the cheapest model that passes your eval bar, and escalate to the expensive one only when confidence is low or a validator rejects the cheap answer. Done right you keep top-tier quality on the hard cases while cutting the bill on the easy majority that never needed the firepower.

Sources:

38. The Multi-Agent Tax

Category: Architecture & Operations

Tagline: Every extra agent multiplies your token bill, so make sure the task can pay it.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

A multi-agent research system can burn roughly 15 times the tokens of a single chat, and token usage alone can explain most of the difference in performance. So multi-agent only makes economic sense when the task is high value and the work genuinely parallelizes. For most tightly coupled work, the coordination overhead isn't worth it.

Warning signs:

Fix patterns:

Worked example:

Impressed by a coordinator-and-subagents demo, you refactor your invoice-processing pipeline into five specialist agents that chat to reach consensus. The work is tightly sequential, so they mostly wait on each other while your token bill jumps roughly fifteen-fold for output no better than one well-prompted pass. Multi-agent only earns its keep when the task is high-value and genuinely parallelizes, like fanning out independent research threads. For tightly-coupled work, the coordination overhead is pure tax: keep it a single agent.

Sources:

39. Your Architecture Mirrors Your Org Chart

Category: Architecture & Operations

Tagline: You ship a system shaped like your teams, so design the teams first.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Any system's structure ends up mirroring the communication structure of the organization that built it. For AI, that means if three teams each own a model, you'll get three agents and a brittle seam between them, whether or not the problem wanted to be split that way. The agent boundaries you ship will trace your team boundaries unless you fight it on purpose.

Warning signs:

Fix patterns:

Worked example:

Three teams each own a model, so the system ships as three agents with a brittle handoff between them, even though the actual task wanted to be one coherent flow. Months later the seams between those agents are where every production bug lives, because each boundary was drawn around a team, not around the problem. Before you commit agent and service boundaries, ask whether they reflect the work or just your reporting lines, and be willing to reshape the teams to get the architecture you actually want.

Sources:

40. Retries Demand Idempotency

Category: Architecture & Operations

Tagline: If an action can run twice, a retry will eventually run it twice.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Agents retry on timeouts, rate limits, and transient errors, but a failed call that never returned may have already succeeded on the server. Without an idempotency key, the retry that 'fixes' a network blip quietly double-charges the card, double-sends the email, or double-books the room. Safe retries depend on the server being able to dedupe.

Warning signs:

Fix patterns:

Worked example:

Your billing agent calls the charge endpoint, the response times out, and the agent's retry logic dutifully fires again. The first call had already succeeded server-side, so the customer gets charged twice and opens an angry ticket. Network blips are routine, so a retry policy without deduplication will eventually double-charge someone. Generate an idempotency key per logical action and pass it on every side-effecting call so the server collapses the duplicate, and never let an agent blindly re-run a non-idempotent operation.

Sources:

41. Trip the Breaker

Category: Architecture & Operations

Tagline: Stop calling the thing that's already failing.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

A downstream model or tool that's timing out doesn't get healthier by being called more. It gets worse, while your agents pile up holding open connections and burning their latency budget. A circuit breaker wraps the call so that once failures cross a threshold it trips, and further calls fail fast instead of hanging, which gives the dependency room to recover.

Warning signs:

Fix patterns:

Worked example:

A downstream embedding service starts timing out, and your agents respond by hammering it harder on every retry, piling up open connections and dragging the whole run's latency into the floor while the sick dependency gets sicker. Calling a failing service more never heals it. Wrap that dependency in a circuit breaker: once failures cross a threshold it trips and calls fail fast instead of hanging, then it periodically probes for recovery. Your agents degrade gracefully on a known error path instead of stalling indefinitely behind a dependency that is not coming back.

Sources:

42. The Ironies of Automation

Category: Humans & Autonomy

Tagline: The more you automate, the harder the leftover human job becomes.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Automation doesn't shrink the human role. It reshapes it into the hardest parts: passive monitoring plus rare, high-stakes intervention. Worse, by taking over the routine work, automation erodes the very skills and situational feel the operator needs when control finally lands back in their lap. You design away the easy 95% and leave humans the 5% they're now least ready to handle.

Warning signs:

Fix patterns:

Worked example:

You ship an invoice-processing agent that handles 95% of documents flawlessly, so the AP clerk now just watches a queue and approves the rare exceptions it kicks out. Six months later a malformed multi-currency invoice lands in their lap and they have no idea how to read it: they have not manually processed one since launch, and the agent gives them a half-finished extraction with no context on why it bailed. Do not dump the gnarly 5% on an operator whose skills you have quietly let atrophy. Keep them in the loop on a sample of normal cases too, and when you hand back, hand back the full reasoning trace and a clear statement of exactly what is stuck.

Sources:

43. Automation Bias

Category: Humans & Autonomy

Tagline: People will trust the machine over their own eyes.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Give people an automated aid and they make errors of omission (missing problems it didn't flag) and commission (following its recommendation even when their own valid evidence says otherwise). The automation becomes a shortcut that replaces careful checking, so the agent's recommendation doesn't just inform the human. It overrides their independent judgment.

Warning signs:

Fix patterns:

Worked example:

Your fraud-review agent flags a transaction as low risk, auto-approve and presents that verdict as a single green badge. The analyst clicks approve without opening the underlying signals, even though the shipping address changed three minutes after a password reset, a pattern they would have caught in a heartbeat on their own. If the recommendation is the only thing on screen, you have built a rubber-stamp machine, not a decision aid. Put the raw evidence next to the verdict, make 'I disagree' a one-click action with no friction, and occasionally withhold the recommendation entirely to keep the human actually looking.

Sources:

44. Match the Level to the Stakes

Category: Humans & Autonomy

Tagline: Full autonomy is a setting, not a default.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Autonomy is a spectrum, from 'the computer suggests' to 'the computer acts and then tells you' to 'the computer acts and decides whether to tell you at all'. The highest levels are a bad idea for consequential actions, because no aid is perfectly reliable and the cost of a confident error has no ceiling. Autonomy isn't one switch. It's a dial you set per action, based on how reversible and costly that action is.

Warning signs:

Fix patterns:

Worked example:

Your support agent has one autonomy setting: act and report. That is fine when it is resending a receipt, but the same dial lets it issue a $4,000 refund and cancel an enterprise subscription before anyone sees it. The fix is not a global require-approval flag that buries humans in confirmations for trivial actions, it is gating per action by reversibility and blast radius. Let it resend receipts and reset passwords autonomously, route refunds over a threshold and any cancellation to propose-and-confirm, and you spend human attention only where a confident error actually costs you.

Sources:

45. Mind the Mode

Category: Humans & Autonomy

Tagline: Most automation surprises start with 'what mode is it in?'

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Flexible, multi-mode automation produces 'automation surprises', where the system does something unexpected because the operator lost track of which mode it was in, what it would do next, and why. As autonomy grows, the human's job shifts to tracking that state, and every hidden mode change becomes a latent failure path. An agent that silently changes how it behaves leaves its supervisor one step from being wrong about it.

Warning signs:

Fix patterns:

Worked example:

Your coding agent silently switches from plan mode to auto-apply edits after a tool result, and the developer, still thinking it is drafting a proposal, watches it rewrite twelve files and run a migration. The surprise is not that it acted, it is that nobody knew which mode it was in or what it would do next. An agent that changes how it behaves without announcing it leaves its supervisor one step from being wrong about it. Render the current mode, the active guardrails, and the next intended action somewhere always visible, and make every mode transition an explicit, loud event the human has to see.

Sources:

46. The Handoff Is the Hard Part

Category: Trust & Coordination

Tagline: In multi-agent systems, failures live in the seams.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Each agent can be flawless on its own and the system still breaks, because the bug lives between them: what got passed, what got dropped, who owned the state. Sub-agents don't inherit context automatically. Anything you don't explicitly hand over simply doesn't exist on the other side.

Warning signs:

Fix patterns:

Worked example:

Your orchestrator spawns a research sub-agent and a writer sub-agent, each flawless in isolation, yet the final report cites a competitor's pricing the user never asked about. The bug lives in the seam: the orchestrator passed the topic but dropped the user's 'EU market only' constraint, and the writer had no way to know it ever existed. Sub-agents do not inherit context by osmosis; anything you do not explicitly pass simply does not exist on the other side. Define the contract at every boundary, hand over the full constraint set and source set deliberately, and validate what crosses instead of trusting it survived the trip.

Sources:

47. Trust Is Calibrated, Not Granted

Category: Trust & Coordination

Tagline: Autonomy is earned in proportion to track record.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

People give an agent freedom the way they give it to a new hire: a little at a time, on reversible things first, loosening the leash only as it proves itself. Both failure modes are real. Over-trust leads to misuse, under-trust leads to a good capability being abandoned. Reliance follows the reliability a system appears to have, not just the reliability it actually has.

Warning signs:

Fix patterns:

Worked example:

Two failure modes, both expensive. On day one you give the agent direct write access to production billing and it confidently double-applies a discount rule across 800 accounts. Or, burned by that, you wire every single action through manual approval, the team drowns in confirmation fatigue, and within a month they have quietly stopped using a genuinely capable tool. Calibrate instead of swinging between extremes: start it on reversible, low-stakes actions, widen the leash as its track record proves out, and surface where it is reliable versus where it is guessing so people lean on it exactly where they should and not an inch further.

Sources:

48. The Escape Hatch Law

Category: Trust & Coordination

Tagline: No clean exit means a fabricated one.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

An agent with no legitimate way to say 'I'm stuck' or 'hand this to a human' will invent a path instead. Cornered with no exit, or forced to fill a required field it has no answer for, it makes up something plausible rather than admit the gap. A confident hallucination is the default when honesty isn't an option.

Warning signs:

Fix patterns:

Worked example:

Your intake agent has a required customer_id field and no way to signal it could not find one, so when a query arrives with no match it confidently invents a plausible-looking ID and pipes a ticket into the wrong account's history. Cornered without a clean exit, a model fabricates rather than admits the gap; the hallucination is the default, not the anomaly. Give it a first-class way out: a nullable field, an explicit unknown enum, an escalate-to-human tool it is encouraged to call. When 'I do not know' is a valid, easy answer, you trade confident fabrications for honest gaps you can actually act on.

Sources:

49. Don't Let the Author Be the Judge

Category: Trust & Coordination

Tagline: The thing that made it shouldn't grade it.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

Without an external signal, a model mostly fails to self-correct its own reasoning, and often makes correct answers worse by second-guessing them. The model that produced a flawed plan is the same one judging it, with the same blind spots. Real correction needs an outside signal: a tool result, a test that runs, a different model. 'Reflect and try again' on the same model with no new information is theater.

Warning signs:

Fix patterns:

Worked example:

Your agent writes a SQL query, you prompt it to review your work and fix any bugs, and it cheerfully second-guesses a correct join into a broken one, because it is grading its own reasoning with the exact same blind spots that produced it. Reflection on the same model with no new information is theater: the author cannot see what it could not see the first time. Real correction needs an outside signal. Run the query against a test database, lint it, or hand it to a fresh instance with no memory of the original attempt, and only trust the fixed version once an external check actually passed.

Sources:

50. Preserve Provenance

Category: Trust & Coordination

Tagline: Don't lose where a fact came from.

Audit lens: Look for places where the agent design violates this law in prompts, context assembly, retrieval, memory, tools, evals, permissions, user handoffs, or observability.

Principle:

When findings get summarized and re-summarized, the claim survives but its source, its date, and its uncertainty quietly drop away, until you're holding an assertion you can't verify or defend. Two sources disagreeing isn't noise to flatten. It's signal to keep. A fact without its provenance is just a rumor that carries itself well.

Warning signs:

Fix patterns:

Worked example:

A research agent reads a 2021 blog post and a 2024 official filing, summarizes both into 'revenue is around $40M', and three hops of re-summarization later your final report states that figure as flat fact with no date, no source, and no hint that the two inputs actually disagreed. A claim without provenance is a rumor with good posture: you cannot defend it, audit it, or weigh it. Carry the full tuple through every transformation, claim plus source plus date plus confidence, and when sources conflict, keep both with attribution instead of silently crowning a winner. The disagreement is signal, not noise to flatten away.

Sources:

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