A production agent that fails silently does not simply lose one task. It creates false confidence. A queue item looks handled, a scheduler looks busy, a process looks alive, and the rest of the system routes around a lie. The cost is paid later in stale research, duplicated work, missed follow-up, corrupt state, and debugging sessions that start with no reproducible case.
The dangerous part is not the exception itself. Exceptions are cheap when they are captured, classified, and replayable. The expensive part is the silent-failure window: the time between the first detectable bad run and the first reliable signal that the run was bad. In weak agent systems, that window stretches across days because the monitoring checks process liveness, queue movement, or token output instead of completion truth.
The fix is not more motivational prompting. It is replay-fixture monitoring. A replay fixture is a production-shaped case with a known input, known contract, known allowed side effects, and a deterministic verdict. It is small enough to run repeatedly and realistic enough to exercise the same code path that failed. If an agent cannot pass the fixture, then a green production dashboard is not evidence of health. It is decorative.
The five-day sprint is a control-system repair: capture every run as evidence, convert failures into deterministic fixtures, replay them on a schedule, and make health gates refuse false success.
Most monitoring was designed for services. A service can expose a port, answer a health check, and report latency. An autonomous worker can satisfy all of those signals while making the wrong decision. It can call tools successfully and still use the wrong input. It can write a partial file and mark the task complete. It can retry away the only useful stack trace. It can exit zero after swallowing the exception that mattered.
The minimum agent contract is stricter. Every attempt needs an input envelope: task id, attempt id, model route, prompt version, tool policy, and state references. It needs an execution trace: normalized tool calls, retry decisions, checkpoint events, exception classes, and timeout behavior. It needs a completion artifact: a file, row, diff, report, message id, or other durable object proving the work exists. It needs a verdict: passed, failed, blocked, degraded, skipped, or inconclusive, with a machine-readable reason.
Without those fields, investigation becomes archaeology. The operator searches logs by timestamp, guesses which worker touched the task, reconstructs partial state, and hopes the external response is still visible somewhere. That is not production operations. That is rummaging through debris.
A replay fixture preserves the smallest useful version of the failure. The point is not to simulate the entire world. The point is to freeze a bug in a form that fails before the same class of production task fails again. Unit tests that mock away orchestration are too narrow. Smoke tests that check startup are too shallow. Replay fixtures sit in the middle: bounded, deterministic, and close to the agent path that actually ships work.
The first implementation step is a run record written before meaningful work begins, updated at checkpoints, and closed by a validator. A useful schema includes run_id, task_id, attempt, agent_name, started_at, code_version, model, prompt_version, tool_allowlist, timeout_seconds, input_sha256, state_refs, artifact_refs, status, failure_class, failure_reason, and closed_at.
The status must not be inferred from process exit. It must be assigned by a completion validator. A research agent is not successful because it produced text. It is successful only if it produced the required artifact, cited the required state paths, passed schema validation, and avoided disallowed mutations. A coding agent is not successful because files changed. It is successful only if the intended files changed, forbidden paths stayed untouched, and the targeted verification command passed.
The entrypoint should be wrapped in four boring functions: start_run(), record_event(), record_artifact(), and close_run(). The close path belongs in a finally block so aborted attempts become failed or inconclusive, not invisible ghosts. Tool adapters should normalize outputs before capture. Store full payloads when replay needs them and policy allows it; otherwise store hashes, selected fields, and redacted summaries.
Two rules prevent most silent failure. First, every run that starts must close. A watchdog scans for started records older than timeout plus grace period and marks them failed with failure_class set to run_abandoned. Second, every done verdict must point at evidence. A completion row without an artifact reference is not completion. It is an unverified claim, and the validator should reject it with reasons such as missing_artifact_ref, empty_output, schema_mismatch, or side_effect_unconfirmed.
Replay fixtures should be generated while the evidence is still warm. A failed run already contains the useful material: input envelope, prompt version, state references, normalized tool responses, expected completion contract, and actual bad result. The fixture generator reduces that run into the smallest package that still reproduces the failure.
A practical fixture layout is simple: fixtures/agent_failure_forensics/<fixture_id>/input.json, expected.json, tool_cassettes.json, and notes.json. The input file contains the task envelope and sanitized state payloads. The expected file contains the behavioral contract: required status, artifact shape, state transition, and forbidden mutation. The cassette file contains deterministic responses for external calls that should not be repeated during replay. The notes file records the production run that generated the fixture and the failure class it protects.
The fixture should encode behavior, not implementation trivia. If the agent failed because it omitted evidence paths, the expected contract should say requires_evidence_path: true, not must_call_function_x. If a worker marked a job complete before writing a report, the contract should require artifact_refs.length > 0 and a readable artifact path, not a particular internal call order. This keeps fixtures useful after the implementation improves.
Agent replay must also handle nondeterminism without surrendering to it. Model text varies, tool timing varies, and state evolves. The answer is to separate strict assertions from flexible content. Use schema checks, artifact existence checks, allowed-status checks, bounded diffs, and semantic validators. Do not require identical prose when the actual contract is that the artifact includes three evidence paths, one failure class, and one keep-or-revert decision.
The command should be explicit: agent-replay --fixture fixtures/agent_failure_forensics/missing_artifact_ref --mode cassette. In cassette mode, recorded tool responses are replayed. In live mode, safe read-only dependencies may refresh. In strict mode, any unrecorded network, browser, account, or money-moving operation is blocked. Monitoring should default to cassette or strict. A replay monitor is supposed to detect regressions, not create side effects.
A fixture becomes regression coverage only after it fails against the broken path and passes after the fix. A fixture that never reproduced the original miss is not a monitor. It is a story.
Fixtures are valuable only when they run continuously enough to catch drift. Production agents drift between releases: prompts change, model routes change, tool schemas change, credentials expire, queues back up, files move, and external products alter response formats. Silent failure often comes from this drift rather than from a dramatic deploy.
The monitoring schedule should be tiered. Run critical canaries every fifteen minutes, the broader suite hourly, and the full historical suite daily. The critical set should cover the highest-value path: queue claim, tool output normalization, artifact validation, retry exhaustion, blocked dependency classification, completion verdict, and alert emission.
Replay results should use the same run-record store as production, with run_type set to replay and fixture_id populated. That symmetry matters. It lets the same dashboards and gates compare live attempts with replay attempts. A replay failure is not merely a test failure. It is evidence that the production control loop may be lying.
The monitor should report fixtures_passing, fixtures_failing, fixtures_stale, and silent_failure_window_minutes. Stale fixtures are not green; they are unknown. The silent-failure window is the interval between the first matching bad production run and the first alert that made the class visible. The target is minutes, not days.
The sprint itself stays narrow:
agent-replay, scheduled canaries, and a parseable replay-status artifact.The sprint is successful only if it changes operational truth. Before the sprint, a failed task can hide behind a healthy worker. After the sprint, the same failure creates a durable failed run, a fixture, a replay failure, and a health signal that refuses to lie.
The durable output is forensic memory. Not vague conversational memory. Reproducible evidence: this input failed, this contract was violated, this fixture preserves the case, this monitor runs it, and this gate blocks false health when the behavior regresses.
Keep the system intentionally small. One run-record schema. One validator interface. One fixture format. One replay command. One monitor artifact. One gate integration. That is enough to convert silent failure into visible failure. Visible failure can be prioritized and fixed. Silent failure just rots the system while dashboards stay polite.
Do not start by asking the model to be more careful. Do not add another prompt sentence saying completion matters. Do not assume a larger model will create state discipline. The defect is structural. The agent needs contracts, captured evidence, deterministic replay, and a health gate that treats missing proof as failure.
The same pattern scales across agent types. Research agents need evidence-path fixtures. Coding agents need diff-and-test fixtures. Browser agents need lease-safe navigation fixtures. Queue workers need claim-to-artifact fixtures. Revenue agents need non-destructive side-effect confirmation fixtures. The details change, but the loop stays constant: capture, classify, freeze, replay, monitor, gate.
Milo packages this as the Agent Failure Forensics sprint: five days to install the run capture, replay fixtures, failure classification, monitoring, and health-gate integration that make silent production failure short-lived and hard to repeat.
Five business days, fixed price, full runbook on delivery. Sample deliverables on the sprint page show exactly what you get before you commit.
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