How it works
Agent observability turns a run into a reconstructable sequence of state changes. The harness records what the agent received, what it attempted, what each tool returned, which controls applied, and how execution ended. The useful unit is not a loose stream of log messages. It is an ordered set of events tied to one run, step, tool invocation, and resulting state.
A practical observability loop captures:
- The run identity, model configuration, permissions, budgets, and starting state.
- The context actually presented to the model, including retrieved material and relevant compaction or filtering.
- Each proposed or executed action, with arguments, authorization decisions, timestamps, and attribution.
- Tool results, validation failures, retries, approvals, interrupts, and state transitions.
- The final output, terminal status, unresolved side effects, and available recovery point.
Collection alone is insufficient. Events need stable identifiers, consistent schemas, and links between causes and effects. Sensitive values should be redacted without erasing the fact that a credential, document, or permission was used. The harness should also distinguish observed facts from model explanations. A model-generated rationale may help interpretation, but it is not proof of the mechanism that produced an action.
Why it matters in an agent harness
Agent failures are often trajectory failures rather than obviously bad final answers. An answer may look plausible even though the agent used stale context, ignored a denied permission, retried a non-idempotent action, or continued after a tool returned an unknown outcome. Output-only monitoring misses those conditions.
Observability gives operators a control surface after execution. You can locate the step where evidence disappeared, determine which identity invoked a tool, inspect whether a policy gate ran, and separate model error from orchestration or provider failure. That shortens diagnosis and limits speculative fixes. Without a reconstructable run, teams tend to change prompts because prompts are visible, even when the actual defect sits in context assembly, authorization, state handling, or recovery logic.
It also supports safer interruption and resumption. A stopped run should reveal its last authoritative state, completed side effects, pending actions, and checkpoint lineage. Otherwise, resuming may duplicate work or silently skip an obligation. The same evidence helps decide whether rollback is possible or whether a compensating action is required.
Observability has a cost. Full context and tool payloads can be expensive to retain and may contain secrets or private data. The answer is not indiscriminate logging. It is deliberate capture with retention rules, redaction, access controls, and enough structural detail to preserve causal meaning. If redaction makes a trace impossible to interpret, the system has protected data by discarding operational accountability.
Agent observability vs agent tracing
Tracing is one mechanism within observability. The distinction affects what you build and what conclusions you can support.
| Concern | Agent tracing | Agent observability |
|---|---|---|
| Primary object | Ordered execution spans or events | The operator’s ability to understand and assess a run |
| Typical question | What called what, and when? | Why did the run behave this way, and what can we safely do next? |
| Required context | Timing, hierarchy, inputs, outputs, errors | Traces plus state, policy decisions, permissions, evaluations, and recovery information |
| Main failure | Missing or disconnected events | Plenty of telemetry without an actionable explanation |
A complete trace can still leave an agent unobservable. For example, it may show that a tool was called while omitting the permission decision, the version of retrieved context, or whether the tool’s timeout left an unknown external result. Conversely, observability does not require retaining every token forever. It requires enough faithful evidence to answer the operating questions the harness is expected to support.
The Rifty take
We optimize for reconstructability at control boundaries, not maximum telemetry volume. Every consequential tool call, permission decision, state transition, and recovery choice should leave durable evidence. We accept selective redaction and bounded retention, but not records that report success while hiding how the result was produced or whether side effects remain uncertain.
Implementation checks
- Give every run, step, tool call, approval, and checkpoint a stable identifier.
- Record the effective context and permissions, not merely their configured defaults.
- Separate proposed actions, authorization decisions, executions, and observed results.
- Preserve raw provider or tool status before mapping it into harness-level states.
- Make retries visible and mark whether the underlying operation is idempotent.
- Represent timeouts and lost responses as unknown outcomes when completion cannot be proved.
- Link interrupts and resumptions to an authoritative checkpoint and side-effect ledger.
- Redact secrets by field while retaining event shape, identity, and policy outcome.
- Test whether an operator can reconstruct a failed run without reading application code.
- Alert when expected telemetry is absent; missing evidence must not look like a clean run.
Frequently asked questions
What should an agent observability record contain?
An agent observability record should contain run identity, effective context, model and harness configuration, tool requests and results, permission decisions, state transitions, retries, approvals, interrupts, checkpoints, terminal status, and known side effects. Each event needs stable attribution and ordering so causes can be connected to outcomes.
Is agent observability the same as logging?
No. Logging emits records, while agent observability is the ability to use evidence to reconstruct and assess a run. Unstructured messages may help debugging but rarely preserve causal links, effective permissions, authoritative state, or recovery choices. Logs contribute to observability only when their structure supports operating decisions.
How much model context should an observability system retain?
Retain enough context to establish what information and instructions could influence each consequential decision. That may mean storing an access-controlled snapshot, a redacted representation, or durable references with integrity metadata. Retention should follow explicit privacy and cost limits, but omissions must remain visible rather than appearing complete.
Can an agent be observable without storing chain-of-thought?
Yes. Agent observability should rely on inspectable inputs, actions, tool results, policy decisions, state transitions, and concise outcome records rather than hidden reasoning. A model’s stated rationale can be recorded as commentary, but it should not be treated as authoritative evidence of why the underlying computation produced an action.
How do you test agent observability?
Test it with known failure trajectories, not only successful runs. Deny a permission, interrupt after a side effect, return a malformed tool result, force a retry, and simulate an unknown outcome. An operator should be able to locate the failure, identify affected state, and choose a safe recovery path.