Direct answer
What should a company track for AI agent observability?
AI agent observability should connect each run to an owner, purpose, model, tools, permissions, cost, quality signal, and business outcome. Tokens and traces help diagnose a run, but operating decisions require a joined record that shows whether the work was useful, safe, and worth repeating.
Key takeaways
- Record the business job before collecting technical detail.
- Connect model calls, tool actions, approvals, and outcomes to one run.
- Measure exceptions and intervention, not just averages.
- Keep raw traces available as evidence, not as the primary operating view.
Observability starts with a durable identity
A run cannot be governed if the company cannot say which agent performed it. Give every agent a durable identity tied to a named owner, team, purpose, and lifecycle state. Then attach every run to that identity, even when the underlying model or runtime changes.
This separates the business concept of an agent from a temporary process, API key, or model endpoint. It also makes cost, incidents, and outcomes comparable over time.
Capture the Grid Agent Run Record
The Grid Agent Run Record is the minimum joined record required to explain a piece of AI work. It starts with who or what requested the work, continues through model and tool activity, and ends with a result that can be accepted, rejected, or measured. Keep one run ID across the entire path so an operator can move from an exception to its evidence without reconstructing the workflow from separate systems.
- Request
- Initiator, task type, input source, and business context.
- Execution
- Model version, prompt version, retrieval sources, latency, and retries.
- Tool use
- Tools selected, arguments, permissions used, side effects, and errors.
- Decision
- Approvals, policy checks, escalations, and human interventions.
- Result
- Output status, quality signal, downstream state, and business outcome.
Minimum Grid Agent Run Record: run ID + agent identity + owner + task + model and prompt version + tools and permissions + approvals + cost and latency + result + quality signal + business outcome.
Use metrics that answer operating questions
Dashboards should help a person decide what needs attention. Group metrics around reliability, quality, access, economics, and outcomes rather than exposing every event at the same level. Cost becomes useful when it is joined to the accepted outcome used to measure AI agent ROI.
| Operating question | Metric | Why it matters |
|---|---|---|
| Is it working? | Completion and failure rate | Shows whether work reaches a terminal result |
| Is it good? | Accepted-result and correction rate | Separates finished work from useful work |
| Is it safe? | Denied actions and permission exceptions | Surfaces access and policy pressure |
| Is it efficient? | Cost and latency per accepted result | Connects spend to usable output |
| Is it valuable? | Outcome volume and business impact | Shows why the agent should continue to exist |
Investigate an exception from signal to outcome
Suppose an invoice-matching agent usually finishes in under 20 seconds, but one run crosses the cost threshold and enters the exception queue. A useful investigation does not stop at the expensive model call. It follows the full run record to the operating cause and business consequence.
This illustrative trace shows why the same event should support both technical diagnosis and an operational decision.
| Step | Observed evidence | Operating interpretation |
|---|---|---|
| Request | Invoice 1842 entered the AP queue | Normal in-scope work |
| Retrieval | Purchase order lookup returned no result | Missing source data |
| Tool action | Lookup retried three times | Retry policy amplified latency and cost |
| Decision | Agent escalated after 46 seconds | Escalation worked, but too late |
| Outcome | Reviewer linked the correct PO and accepted the match | Useful result after human intervention |
Action: fix the source-data lookup and lower the retry limit. Do not respond by changing the model until the run record shows the model caused the failure.
Set AI agent alert thresholds around action
An alert should identify a condition that requires a specific owner to decide or act. Set thresholds against the workflow's normal range and risk level; a single universal latency, cost, or failure threshold will produce noise across agents that do different jobs.
| Signal | Example trigger | Required action |
|---|---|---|
| Reliability | Failure rate exceeds baseline by 5 points for 30 minutes | Inspect the failing step and pause expansion |
| Quality | Accepted-result rate falls below the approved bar | Route work to review and open a regression check |
| Economics | Cost per accepted result rises 25% week over week | Separate volume, retry, routing, and price effects |
| Access | A new permission or denied high-risk action appears | Confirm authorization before the next attempt |
| Operations | Approval or exception remains unresolved past its service target | Assign or escalate to the accountable owner |
Thresholds above are examples, not defaults. Version each rule with its owner, evidence window, severity, and expected response so alerts remain auditable as the workflow changes.
Treat exceptions as the primary queue
Averages can look healthy while a small set of runs creates serious risk. Put failed runs, unusual spend, repeated retries, new permissions, quality regressions, and unresolved human reviews into one ranked queue. Each exception should explain why it was flagged and what evidence supports the flag.
This turns observability into an operating practice. Teams can assign an owner, resolve the issue, and preserve the decision instead of watching a graph and opening a separate investigation elsewhere.
Keep traces as an evidence layer
Traces are essential when an operator needs to understand why a run failed. They should preserve model calls, tool steps, timing, and errors with appropriate redaction. OpenTelemetry semantic conventions provide a shared naming model for spans, metrics, logs, and events. But a trace is not the homepage for an AI workforce program.
Lead with the operating question and link to the trace when deeper evidence is needed. This keeps the main view useful to business owners while preserving the technical detail engineers need for diagnosis.
Instrument for learning, not surveillance
Collect the minimum content needed to evaluate and improve the system. Prefer structured events, references, classifications, and redacted payloads over indiscriminate prompt logging. Define retention and access rules for run data, especially when work touches customer, employee, or financial information.
The goal is accountable work: enough evidence to understand what happened, protect the company, and improve the workflow without turning observability into unnecessary data collection. The NIST AI RMF Measure function similarly emphasizes documented measurement in deployment-like conditions and continued monitoring across the system lifecycle.
Evidence trail
Sources and methodology
Grid Field Notes combines Grid's operating model with published technical and risk-management guidance. Numerical examples are illustrative unless an article explicitly identifies measured customer data.
Frequently asked questions
Questions teams ask
What is AI agent observability?
AI agent observability is the ability to understand an agent's requests, model calls, tool actions, decisions, costs, quality, and outcomes as one connected run record.
Are token counts enough to monitor an AI agent?
No. Token counts explain model consumption, but not whether the agent used the right access, completed the job, required human correction, or created a business result.
What should trigger an AI agent alert?
Alert on conditions that need action: repeated failures, quality regressions, unusual cost, risky permission use, stalled approvals, and missing or deteriorating outcomes.