Governance & Observability

Persist agent execution state so a long-running run survives restarts, deploys, or user disconnects.

Run a held-out dataset against agent versions to detect regressions and measure improvement.

Persist the agent's reasoning trace alongside its actions so post-hoc review can explain why.

Use an LLM to score open-ended outputs against rubric criteria when no exact-match metric applies.

Re-run a past agent trace from any step with modified inputs/prompts/tools to debug or branch.

Log every agent decision and state change with enough metadata to explain or reverse it later.

Evaluate an agent's full trajectory (steps, tool calls, intermediate states) by another agent rather than scoring only the final output.

Track which prompt version, model version, and data sources produced each agent output.

Run a candidate agent version in shadow alongside the champion, comparing outputs without affecting users.

Run two parallel evaluation tracks — offline benchmark gates before deploy AND online production-traffic monitoring after — so drift is caught even when pre-deploy benchmarks pass.

Surface per-request, per-user, and per-feature cost and token consumption to operators in near-real-time.

Treat the eval suite as the contract the agent must satisfy; releases ship only if evals pass.

Treat prompts as immutable, hashed, semver'd artefacts in a registry; deploy and roll back like code.

Wrap every model call, tool call, and memory access in a composable pre/execute/post interceptor pipeline so cross-cutting concerns attach without touching agent or orchestrator code.

Capture workflow execution state as a snapshot in a pluggable storage provider so a paused run can resume across deployments, process restarts, and host crashes.

Gate a support agent on a re-contact-subtracted resolution rate so an interaction that merely ends the session is never reported as a resolved one.

Treat sandbox boundary violations as telemetry; alert on syscalls, network egress, or filesystem writes outside expected scope.

Record the output of every non-deterministic step on first execution and replay that recorded value during crash-recovery instead of re-invoking the model.

Sort every production agent failure into a small fixed taxonomy and bind each class to a set remediation path, so fixes are dispatched mechanically and the monitor-to-fix loop stays fast enough to ga…

Capture full prompt/response/metadata traces from production into a monitoring dataset, but only run LLM-judge evaluation on a random sample so monitoring cost stays bounded as traffic grows.

Score agent outputs asynchronously in production with non-blocking scorers that observe, alert, and log but do not regenerate the output.

A dedicated agent or harness whose sole job is running tests against another agent's outputs to evaluate performance; distinct from eval-harness (offline batch) and llm-as-judge (per-output).

Manufacture agent instances from a versioned template that renders model, tools, and prompt atomically, with registry-backed identities, so a fleet stays consistent and one template change propagates…

Replace fixed-split A/B tests between agent variants with a bandit that dynamically reallocates traffic toward better-performing variants based on observed reward, bounding regret from bad variants.

Mint a self-contained record at the moment a consequential action executes, bundling the rule that fired, the exact data read, the conclusion reached, and the authorizing identity.

Evaluate intermediate artifacts (plans, tool-call traces, guardrail reactions) not only final outputs; isolates failure to a specific pipeline node.

Offer the agent loop itself as a managed cloud primitive so a caller supplies a model, system prompt, and tools and the platform runs the orchestration in an isolated, session-scoped runtime.

Every prompt in a production agent is versioned, tested, and owned by the team in the application repo — never inherited as a framework default.

On each model upgrade, treat every harness component as an encoded assumption about a model weakness and ablate the components the new model no longer needs, gated by evals.

Reify obligations, permissions, and prohibitions as transferable deontic tokens that agents pass along the delegation chain with provenance, so duty and accountability transfer with the work, not onl…

When an agent serves multiple humans with conflicting preferences, declare the aggregation rule explicitly rather than letting it be implicit in the prompt or fine-tune.

Constrain an agent to the platform's curated golden path of living, machine-readable standards and check for drift as it works, so its output is compliant by construction rather than corrected later.

Require an external regulator to certify the generative service against a published content-safety standard before it may serve the public, forcing the standard's controls into the build as a re-cert…

Mine a corpus of thousands of written postmortems through a staged model pipeline that summarises, classifies, analyses, and aggregates so that recurring incident causes surface as one short report.

Relocate verification rigor from the model loop to surrounding scaffolding (evals, judges, decision logs, policy gates) so failures are caught by the wrapper rather than the agent.

Anti-pattern: let a well-performing pilot quietly expand in scope until it is a de facto production decision system, while keeping the 'pilot' label so it never trips the go-live governance gate.

Surface which input tokens caused a given output by perturbing attention across all transformer layers and measuring the resulting change in output probability, producing a per-token relevance map al…

Classify an agent into a reproducibility tier by re-running identical inputs, require the strictest decision-determinism tier for regulated decisions, and gate deployment and validation-sample size o…