Governance & Observability

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

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

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

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

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.

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

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

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.

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

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

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.

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

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

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.

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.

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

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…

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.

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.

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.

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

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

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.

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

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…