AI Engineer · Published 2026-10-18

Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS

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agentsverificationplaywrightevalsreceiptschrome-devtools-protocolfailure-modesci-gates
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Summary

Overview

  • Speaker: Nick Nisi & Zack Proser
  • Channel: AI Engineer
  • Main topic: Building verification loops and proof mechanisms for AI coding and task agents
  • Purpose: To teach developers how to implement verification loops, use deterministic checks instead of LLM self-reporting, and build trust in AI coding agents. Nick Nisi and Zack Proser from WorkOS present a workshop on 'Beyond Done', addressing the critical problem of AI agents claiming tasks are complete without proper verification. They discuss how self-reported 'done' leads to catastrophic failures in production and advocate for building verification loops, making user journeys executable via CLI tools (like Playwright and Chrome DevTools Protocol), recording evidence (receipts, screenshots, console logs), and utilizing System 1 and System 2 evaluators to ensure agent reliability and scale.

Topic Map

The Problem with Self-Reported Done

  • Explanation: AI agents often execute tasks quickly and report success based on code generation and passing unit tests, but without verifying actual runtime UI state, persistence, or side effects, leading to silent failures.
  • Key claims:
    • An agent can write code that builds and passes tests while still being functionally wrong.
    • Relying on LLM self-reported 'done' creates a massive signal-to-noise ratio problem for human reviewers.
  • Examples:
    • An agent deleting production databases in seconds because instructions did not enforce boundaries.
  • Terminology:
    • Self-reported done
    • Shallow check
    • Signal-to-noise ratio
  • Why it matters: Without verification, trusting agents at scale leads to catastrophic software failures.

Building the Verification Loop

  • Explanation: To trust AI agents, developers must build loops consisting of four phases: Build the check, give the agent a way to run it, keep the evidence, and decide when to stop.
  • Key claims:
    • If an agent can't verify its own work, nothing else matters.
    • Verification is the key foundational step to achieving true scale with AI agents.
  • Examples:
    • Running Playwright scripts via CLI to check UI state after reload.
  • Terminology:
    • Verification loop
    • Determinism
    • Evaluator
  • Why it matters: Enables developers to safely automate agentic workflows without babysitting every line of code.

Providing Agent Eyes via Chrome DevTools Protocol

  • Explanation: Giving agents CLI tools (like 'snack') that speak the Chrome DevTools Protocol lets them inspect browser state, take screenshots, check local storage, and query console logs.
  • Key claims:
    • LLMs are brains; CLI tools and browser protocols give agents eyes and a body.
    • A screenshot of success is not proof if the underlying state vanished on reload.
  • Examples:
    • Using 'pnpm snack snapshot' and 'pnpm snack storage' to verify waitlist email persistence.
  • Terminology:
    • Chrome DevTools Protocol
    • Receipt
    • Proof
  • Why it matters: Allows agents to observe runtime application state independently of their own generated code.

Accumulating Receipts and System 1 / System 2 Evaluation

  • Explanation: Every agent run produces a receipt containing version hashes, screenshots, checks passed, and error logs. These receipts feed into downstream evaluators (System 1 fast checks and System 2 judges) to enable self-healing loops.
  • Key claims:
    • Status comes from records, not model prose.
    • Failures drive future work by narrowing the search space for agent retries.
  • Examples:
    • Comparing snack receipts against expected UI states across multiple iterations.
  • Terminology:
    • Receipt
    • System 1
    • System 2
    • Evaluator
  • Why it matters: Accumulated run receipts build an invaluable dataset for continuous agent improvement and automated regression testing.

Key Points

Status comes from records, not model prose

  • Explanation: Never trust an agent's text response claiming a task is done; require cryptographic or programmatic proof (receipts, screenshots, test results).
  • Evidence: Real-world examples where agents reported success while local storage was empty.
  • Practical implication: Enforce strict verification gates in CI/CD pipelines before accepting agent output.

Make the user journey executable

  • Explanation: Wrap UI interactions into deterministic CLI commands that agents can execute independently.
  • Evidence: Using Playwright scripts combined with local storage inspection tools.
  • Practical implication: Reduces human review overhead and accelerates agentic development velocity.

Receipts accumulate for future evals

  • Explanation: Storing run attempts, failures, and proofs creates a curated dataset for fine-tuning and future agent runs.
  • Evidence: Tracking attempt numbers, revisions, and page errors in JSON receipt structures.
  • Practical implication: Improves agent success rates over time through historical context.

Frameworks, Models & Processes

The Verification Loop

  • How it works: A four-step framework to ensure agent reliability by building checks, executing them, retaining evidence, and defining clear stopping criteria.
  • Components:
    • Build the check
    • Give the agent a way to run it
    • Keep the evidence
    • Decide when to stop
  • When to use: Whenever deploying AI coding agents or autonomous workflows that perform side effects.

Examples & Case Studies

An agent signed up for a waitlist in a browser app, saw a success message, but failed to persist the email to local storage upon page reload.

  • Illustrates: Shallow checks vs. deep state verification.
  • Lesson: UI success messages do not prove data persistence; state must be verified independently.

Actionable Takeaways

  • Immediate:
    • Stop relying solely on LLM self-reporting for task completion.
    • Implement browser automation and snapshot tools for agents.
  • Strategic:
    • Treat agent run receipts as valuable training and evaluation datasets.
    • Shift from micro-managing PRs to managing verification gates and guardrails.
  • Questions to investigate:
    • How do we balance agent creativity with strict verification constraints?
    • What is the optimal threshold for automated agent retries before human escalation?

Claims Worth Verifying

  • An agent can write code that builds and passes tests while still being functionally wrong. (empirical observation)
  • Status comes from records, not model prose. (architectural principle)

Notable Quotes

"If an agent can't verify its own work, nothing else matters." "Status comes from records, not model prose."

Compressed Summary

  • AI agents frequently report tasks complete without verifying runtime persistence.
  • Verification loops require executable checks, evidence collection, and clear stopping rules.
  • Chrome DevTools Protocol and Playwright provide agents with observable application eyes.
  • Run receipts accumulate verifiable proof to replace model prose with hard data.
  • Keywords: agents, verification, playwright, evals, receipts
  • Core insight: AI coding agents must be anchored by deterministic verification loops and immutable run receipts to ensure true production reliability.

Core insights

7
Architecturemedium noveltymoderate evidence

Task completion status should be derived from an immutable, machine-checkable artifact (a 'receipt' containing version hashes, screenshots, checks passed, error logs) rather than from the agent's own text claim of success; the model's prose is treated as an untrusted assertion.

Why it matters

It relocates the source of truth for agent state out of the model's context and into an external record store, which makes completion auditable, replayable, and machine-gateable — a prerequisite for running agents without per-step human supervision.

Generalization

Any agent with side effects (code changes, data writes, deployments) should emit a structured evidence record as the only accepted proof of completion; the agent becomes a proposer and the record system becomes the arbiter.

Status comes from records, not model prose.
Open source video
require cryptographic or programmatic proof (receipts, screenshots, test results)
Open source video
Failure Modehigh noveltystrong evidence

The central failure mode is the shallow check: an agent produces code that builds and passes unit tests, and a UI that displays a success message, while the underlying state (e.g., local storage persistence) does not survive a reload. Passing the observable surface is not evidence of side-effect correctness.

Why it matters

It shows where verification must be placed: after the state transition and across a reload/restart boundary, not at the level of generated code or transient UI feedback. Verifiers placed at the wrong depth produce confident false positives.

Generalization

Verification depth must match the durability of the effect being claimed: for persistence claims, check state after a reload/restart; for network claims, check the server, not the toast.

A screenshot of success is not proof if the underlying state vanished on reload.
Open source video
UI success messages do not prove data persistence; state must be verified independently.
Open source video
Mechanismmedium noveltymoderate evidence

Agents need an observation channel independent of the code they just wrote. Exposing the Chrome DevTools Protocol through purpose-built CLI tools ('snack' commands for snapshot, storage, console logs) gives the agent eyes into runtime state — screenshots, local storage, console errors — instead of relying on its own generated source as the model of reality.

Why it matters

It defines a concrete tool-bandwidth boundary: the agent's perception of the app comes from a protocol-level probe, so the same signal works for verification, debugging, and retry guidance rather than being tangled with the agent's own output.

Generalization

Give agents a low-level, deterministic instrumentation interface to their own artifacts (browser protocol, DB introspection, log query) so verification and diagnosis share one observation path.

LLMs are brains; CLI tools and browser protocols give agents eyes and a body.
Open source video
Using 'pnpm snack snapshot' and 'pnpm snack storage' to verify waitlist email persistence.
Open source video
Practicemedium noveltymoderate evidence

The user journey itself should be compiled into executable, deterministic CLI commands that the agent runs autonomously, converting a manual QA narrative into a reusable check the agent can invoke on every iteration.

Why it matters

It creates a durable asset (an executable journey) that outlives any single agent run and can be composed into CI gates, turning verification from a per-task human activity into infrastructure.

Generalization

The unit of agent-verifiable work is an executable scenario, not a description of desired behavior; if a requirement cannot be expressed as a runnable check, the agent cannot close the loop on it.

Wrap UI interactions into deterministic CLI commands that agents can execute independently.
Open source video
Running Playwright scripts via CLI to check UI state after reload.
Open source video
Mechanismhigh noveltymoderate evidence

Receipts accumulate into a dataset: attempt numbers, revisions, page errors, screenshots, and pass/fail records across iterations become both a historical context for narrowing the retry search space and a corpus for downstream System 1 (fast deterministic checks) and System 2 (judge) evaluation.

Why it matters

It joins two normally separate systems — the agent runtime and the eval pipeline — so production runs continuously generate evaluation material, and failure history becomes an input to the next attempt instead of discarded noise.

Generalization

Treat every agent attempt as a labeled data point; failed attempts with recorded error evidence are the highest-value training and retry-guidance signal because they localize where the search should not go.

Failures drive future work by narrowing the search space for agent retries.
Open source video
Tracking attempt numbers, revisions, and page errors in JSON receipt structures.
Open source video
Mental Modelmedium noveltymoderate evidence

The verification loop is a four-phase structure with an explicit termination phase: build the check, give the agent a way to run it, keep the evidence, and decide when to stop. Stopping criteria are treated as a designed component rather than an emergent property of the agent.

Why it matters

Most agent-loop designs specify tooling and evidence but leave halting to the model; making 'when to stop' a first-class phase is what prevents infinite self-healing loops and unbounded cost.

Generalization

Any autonomous retry loop needs an externally owned termination policy (attempt caps, escalation thresholds) defined outside the model that is being retried.

Build the check
Open source video
Give the agent a way to run it
Open source video
Keep the evidence
Open source video
Decide when to stop
Open source video
Predictionmedium noveltyweak evidence

When agents self-report completion, human review cost is dominated by false positives, producing a signal-to-noise problem; the remedy is to shift human effort from reviewing individual PRs to designing verification gates and guardrails.

Why it matters

It redefines the engineer's job in an agentic workflow — the leverage point moves to authoring checks and boundaries, which is a different skill and a different artifact than code review.

Generalization

As agents scale, engineering output shifts toward specifying invariants and gates; review capacity becomes the bottleneck and is relieved by automation of the accept/reject decision, not by better summaries.

Relying on LLM self-reported 'done' creates a massive signal-to-noise ratio problem for human reviewers.
Open source video
Shift from micro-managing PRs to managing verification gates and guardrails.
Open source video

Deep dives

5

Verification depth versus the durability boundary

Research question

At what depth must a check be placed — generated code, transient UI text, post-reload state, or backend store — for each class of agent claim (persistence, network write, deployment), and how often do shallower checks produce confident false positives?

Why

If verifiers sit above the durability boundary, an agent that satisfies build and unit-test gates will still be functionally wrong at runtime, and the false confidence scales linearly with agent volume. Knowing the minimum correct probe depth per claim type is what makes an accept/reject gate sound rather than decorative.

A screenshot of success is not proof if the underlying state vanished on reload.
Open source video
UI success messages do not prove data persistence; state must be verified independently.
Open source video
An agent can write code that builds and passes tests while still being functionally wrong.
Open source video
Source video

Externally owned termination policy for self-healing loops

Research question

Given a receipt stream with attempt counts and failure classes, where does marginal retry success probability flatten, and what escalation rule (attempt cap, failure-class allowlist, cost budget) beats a fixed retry count?

Why

The four-phase loop names 'decide when to stop' as a phase but leaves halting to the model by default, which is how unbounded retry and late escalation happen. A measured stopping curve turns termination from an emergent property into infrastructure.

Decide when to stop
Open source video
Keep the evidence
Open source video
What is the optimal threshold for automated agent retries before human escalation?
Open source video
Failures drive future work by narrowing the search space for agent retries.
Open source video
Source video

Invariant-level versus path-level verification

Research question

Do outcome-level checks (state invariants, persistence, side effects) preserve agent solution diversity while still rejecting shallow successes, compared with path-level checks that encode one expected interaction sequence?

Why

Strict verification trades false positives for false negatives; if the strictest gate only accepts the encoded path, the agent's ability to find unanticipated valid solutions is silently destroyed, and verification becomes a ceiling on capability rather than a floor on correctness.

How do we balance agent creativity with strict verification constraints?
Open source video
Wrap UI interactions into deterministic CLI commands that agents can execute independently.
Open source video
Source video

Receipt schema and tamper-evidence as a long-lived contract

Research question

What minimal receipt schema (artifact/version hash, probe type, expected invariant, observed value, environment, check-code hash) makes completion auditable and replayable across browser UI, database mutation, API deployment, and filesystem side effects, and what forge/replay attacks does it resist?

Why

If receipts become the authority for status and simultaneously the eval dataset, the schema stops being an implementation detail and becomes the durable interface between runtime, CI gates, and evaluation; a weak schema certifies nothing and an unstable one breaks both audits and evals on every revision.

Tracking attempt numbers, revisions, and page errors in JSON receipt structures.
Open source video
Using 'pnpm snack snapshot' and 'pnpm snack storage' to verify waitlist email persistence.
Open source video
Accumulated run receipts build an invaluable dataset for continuous agent improvement and automated regression testing.
Open source video
Source video

Failure receipts as retry context: signal or noise

Research question

Does injecting prior failure receipts (failure class, page errors, observed versus expected state) into the next attempt's context increase retry success rate at an equal attempt budget, or does it merely anchor the model on the failed path?

Why

The claim that failures narrow the search space is asserted, not measured; if it holds, the retry loop gets cheaper and receipts serve a second purpose beyond audit, and if it fails, evidence storage is purely forensic and the loop design must change.

Failures drive future work by narrowing the search space for agent retries.
Open source video
If an agent can't verify its own work, nothing else matters.
Open source video
Source video

Article ideas

4

Your Tests Passed and Your App Is Broken: The Verification Depth Problem

Build-and-test gates certify that an agent authored plausible code, not that the effect it claims actually happened; until verification is anchored past the durability boundary (reload, restart, server read), agent completion signals are structurally unreliable and get worse as volume grows.

Angle

Show the exact depth ladder a check can be placed at, demonstrate which failure each depth catches and misses, and argue that 'it compiles and tests pass' is a statement about authorship, not outcome.

Source video

Receipts, Not Prose: Why an Agent's 'Done' Must Be a Record

The agent's natural-language completion claim should be treated as an untrusted assertion and demoted to advisory; status must be derived from an external evidence record keyed to version hashes, screenshots, checks passed, and error logs, because only a record can be gated, replayed, and audited without re-running the agent.

Angle

Design-level argument: move the source of truth out of the model context into a receipt store, then show what becomes possible (CI gates, post-hoc audit, eval corpus) and what breaks (nothing except the habit of trusting the summary).

Source video

Done Is a Policy: Designing the Stop Condition of a Self-Healing Loop

Most agent-loop designs specify tools and evidence and leave halting to the model, which is why self-healing becomes unbounded retry; 'when to stop' must be an externally owned, designed phase with attempt caps and failure-class escalation thresholds, not an emergent property of the thing being retried.

Angle

Take the four-phase loop and zoom in on the phase everyone skips, framing termination as cost control and safety rather than as a UX detail.

Source video

Your Evals Are Already Running in Production — You're Deleting Them

Every agent attempt is a labeled data point; treating the run log as the eval dataset collapses runtime observability and offline evaluation into one schema, so the practical move is not to build a better curated eval set but to stop discarding the failure evidence the runtime already produces.

Angle

Argue the pipeline-merge: production receipts feed System 1 deterministic checks and System 2 judges, and failed attempts are the highest-value signal because they localize where the search should not go.

Source video

Project ideas

4

Receipt Gate

gatehouse

A receipt-gated accept/reject decision reduces false-positive completions relative to trusting agent self-report by a large margin, and the reduction grows with agent volume rather than shrinking.

Proof of concept

Take a task suite of agent-generated changes (build + unit tests passing), run each against a receipt store that requires version hash, executable journey result, post-reload state probe, and console error log; gate acceptance on the receipt and compare against a self-report-trusting baseline.

Measurement

False-positive completion rate (accepted-but-runtime-broken) and reviewer time-to-accept per accepted change, gated versus ungated, over the same task suite.

Source video

Persistence-Depth Harness

movement-lab

A material fraction of agent runs that pass a screenshot/UI-success check fail a post-reload state probe, so screenshot-level proof alone is insufficient evidence of persistence.

Proof of concept

Build a CLI probe layer over the Chrome DevTools Protocol exposing snapshot, storage, and console-log subcommands; run a waitlist-style flow (submit, see success message, reload, read local storage/backend) and record both the shallow and deep verdicts per run.

Measurement

Percentage of runs where the shallow check passes and the post-reload probe fails, plus which probe type (storage, console, network) catches each failure.

Source video

StopPolicy Bench

beyond-evals

Marginal retry success probability flattens well before typical fixed retry caps, and a failure-class-aware escalation rule reaches the same success rate at materially lower cost than a flat attempt cap.

Proof of concept

Instrument a verification loop to log attempt number, failure class, and outcome per attempt into receipts; replay the resulting curves offline to fit an escalation policy, then A/B flat-cap versus failure-class-aware halting on a held-out task set.

Measurement

Success rate at matched attempt cost, cost per solved task, and rate of escalations that arrive after the point where retrying had stopped paying off.

Source video

Receipt Reprojection

new

Injecting prior failure receipts into the next attempt's context increases retry success rate at an equal attempt budget, compared with retrying from the same bare task prompt.

Proof of concept

For a set of failing agent tasks, fork each into two arms with identical attempt budgets: one retries cold, the other receives a structured failure receipt (failure class, observed versus expected state, page errors, screenshots).

Measurement

Retry success rate delta between arms at equal attempt count, plus whether the second arm converges in fewer mean attempts or anchors on the failed path.

Source video

Architectural implications

4

Completion state is currently asserted inside the agent's response text.

Before

The agent's natural-language 'done' message is the status, and a human or downstream system trusts it.

After

A receipt store keyed to version hashes, screenshots, checks, and error logs is the status authority; the agent's prose is advisory only.

Consequence

Verification logic and evidence storage become separate infrastructure from the agent runtime, enabling CI gates and post-hoc audit without re-running the agent.

Source video

Agent observability into the application is indirect, mediated by the agent's own generated code.

Before

The agent reasons about app state from the source it wrote and from transient UI text it can see.

After

A CLI/protocol layer (Chrome DevTools Protocol) exposes snapshots, storage, and console logs as deterministic, agent-callable probes.

Consequence

The same probe interface serves verification, diagnosis, and retry guidance, and decouples agent perception from agent authorship.

Source video

Evaluation is a separate offline pipeline fed by curated datasets.

Before

Evals are built from hand-collected examples disconnected from production runs.

After

Every agent run emits a receipt that flows into System 1 / System 2 evaluators, making the run log the eval dataset.

Consequence

Eval freshness and coverage track production automatically, but receipt schema and retention become critical long-lived contracts.

Source video

Requirements live as prose instructions to the agent.

Before

A user journey is described in a prompt or a ticket, and the agent translates it ad hoc each run.

After

The journey is an executable scenario (Playwright + storage checks) invoked as a CLI command on every iteration.

Consequence

Requirements become versioned, testable artifacts; unexpressible requirements are visibly unverifiable rather than silently unverified.

Source video

Tradeoffs and failure modes

5

Strict verification vs. agent latitude

Benefit

Deterministic checks make completion decidable and eliminate silent failures.

Cost or risk

Over-constraining checks may block legitimate solutions the agent finds outside the encoded path, trading false positives for false negatives.

How do we balance agent creativity with strict verification constraints?
Open source video
Source video

Automated retry loops

Benefit

Failures narrow the search space for subsequent attempts, enabling self-healing without human involvement.

Cost or risk

Without a defined stopping phase, retries can loop indefinitely or escalate late; the escalation threshold is unspecified.

What is the optimal threshold for automated agent retries before human escalation?
Open source video
Source video

Screenshot-based proof

Benefit

Screenshots are cheap and readable evidence of an observed UI state.

Cost or risk

A visual success state is not proof of persistence; verification must survive a reload or it certifies a transient illusion.

A screenshot of success is not proof if the underlying state vanished on reload.
Open source video
Source video

Agent autonomy without enforced boundaries

Benefit

Unbounded instructions let agents act fast and without friction.

Cost or risk

Destructive side effects can happen in seconds when instructions do not enforce boundaries.

An agent deleting production databases in seconds because instructions did not enforce boundaries.
Open source video
Source video

Trusting build-and-test success as completion

Benefit

Test suites are cheap, deterministic, and already in CI.

Cost or risk

An agent can satisfy compile and unit-test gates while remaining functionally wrong at runtime, creating false confidence that scales with agent volume.

An agent can write code that builds and passes tests while still being functionally wrong.
Open source video
Source video

Open questions

5

What is the correct stopping/escalation policy for a self-healing verification loop?

Why unresolved

The summary names 'decide when to stop' as a loop phase and explicitly lists the retry threshold as an open question, without giving a number or policy.

Research direction

Instrument receipt streams with attempt counts and failure classes, then measure marginal success probability per retry to find where the curve flattens and human escalation becomes cheaper.

Source video

How can verification be made strict enough to gate autonomy without suppressing valid alternative solutions?

Why unresolved

The summary poses the creativity-vs-constraint balance as an open question and offers no mechanism for distinguishing a wrong answer from an unanticipated right one.

Research direction

Compare outcome-level checks (state invariants, persistence, side effects) against path-level checks; test whether invariant-style verification preserves solution diversity while still rejecting shallow successes.

Source video

Does receipt-driven verification generalize beyond browser UI flows to backend-only and non-UI agent side effects?

Why unresolved

All concrete evidence in the summary is browser-centric (Playwright, Chrome DevTools Protocol, local storage); other domains are not demonstrated.

Research direction

Define a domain-agnostic receipt schema (artifact hash, probe type, expected invariant, observed value) and validate it on database mutations, API deployments, and file-system changes.

Source video

Can accumulated receipts measurably improve agent success rates, or do they only serve as an audit trail?

Why unresolved

The summary claims failures narrow the search space and receipts build an invaluable dataset, but presents no measurement of improvement.

Research direction

Run A/B comparisons of retry success rate with and without failure-receipt context injected, controlling for attempt budget.

Source video

What makes a receipt trustworthy — how tamper-evident and reproducible must it be?

Why unresolved

The summary calls for 'cryptographic or programmatic proof' and mentions version hashes, but does not specify integrity or reproducibility requirements.

Research direction

Prototype signed receipts binding check code hash, environment, and observed state, then test replay and forgery scenarios.

Source video

Key claims

8
factualVerification needed

An agent can write code that builds and passes tests while still being functionally wrong.

Evidence

An agent can write code that builds and passes tests while still being functionally wrong.

Question

Across a sample of agent-generated changes that pass build and unit tests, what fraction fail runtime state verification, and which check types catch them?

Source video
causalVerification needed

Completion status must come from recorded evidence rather than the model's own prose.

Evidence

Status comes from records, not model prose.

Question

Do receipt-gated accept/reject decisions reduce false-positive completions relative to trusting agent self-report, and by how much?

Source video
causalVerification needed

A success screenshot is not proof of correctness if the underlying state does not survive reload.

Evidence

A screenshot of success is not proof if the underlying state vanished on reload.

Question

What proportion of screenshot-passing agent runs fail a post-reload state check?

Source video
causalVerification needed

Self-reported completion creates a signal-to-noise problem that degrades human review.

Evidence

Relying on LLM self-reported 'done' creates a massive signal-to-noise ratio problem for human reviewers.

Question

How does reviewer precision/time-to-accept change when verification gates precede human review?

Source video
causalVerification needed

Recorded failures narrow the search space for subsequent agent retries.

Evidence

Failures drive future work by narrowing the search space for agent retries.

Question

Does injecting prior failure receipts into context increase retry success rate at equal attempt budget?

Source video
opinionVerification not requested

If an agent cannot verify its own work, no other capability matters.

Evidence

If an agent can't verify its own work, nothing else matters.

Source video
opinionVerification needed

Accumulated receipts form a valuable dataset for evaluation and continuous improvement.

Evidence

Accumulated run receipts build an invaluable dataset for continuous agent improvement and automated regression testing.

Question

What measured lift in eval coverage or agent success is attributable to receipt-derived data versus hand-curated examples?

Source video
factualVerification needed

Without enforced boundaries, agents can cause catastrophic side effects in seconds.

Evidence

An agent deleting production databases in seconds because instructions did not enforce boundaries.

Question

Which boundary mechanisms (tool allowlists, environment scoping, confirmation gates) most reduce blast radius per unit of agent autonomy lost?

Source video

Connections

5