The complete run context
Join model, tool, identity, permission, policy, human, latency, and cost events under one run record.
Operating model
Talk to a human AI agent observability and outcomes
Observe the work, not just the tokens.
Grid connects runtime evidence to the owner, access, cost, intervention, accepted work, and business outcome behind every agent run.
GridOutcome recordLive record
- Run
- Invoice batch · 184 items Complete
- Intervention
- 7 exceptions reviewed Accepted
- Cost
- $14.20 total run cost In plan
- Outcome
- 177 invoices reconciled Measured
01
02
03
04
What observability must connect
A trace can explain a run. It cannot explain whether the work mattered.
Technical telemetry is necessary, but enterprise teams also need the operating context around it: who asked for the work, which authority the agent used, where people intervened, what was accepted, and what changed in the business.
Accepted work over raw output
Measure what entered the workflow, what needed correction, and what the business actually used.
Outcomes with an owner
Keep every metric attached to the team, process, baseline, and decision it is meant to improve.
The operating loop
Connect telemetry to the operating decision.
Grid keeps the technical trace and business result close enough for teams to diagnose failures, control cost, and improve the work.
- 01
Instrument
Capture model, tool, identity, policy, latency, and cost events.
- 02
Correlate
Join those events to one agent, owner, workflow, and run.
- 03
Review
Record intervention, exceptions, accepted work, and failure modes.
- 04
Improve
Use the evidence to change the harness, access, process, or model.
Evidence for the operating model
Start with the field notes behind the work.
Our notes separate what has been observed from what teams should test next.
Common questions
What teams need to know.
01What should AI agent observability track?
Track model calls, tool actions, process events, identities, permissions, policy decisions, human interventions, latency, cost, accepted work, failure modes, and business outcomes under one run and agent identity.
02Why are token counts not enough for AI agent observability?
Tokens describe model consumption, not whether the agent used the right authority, completed the workflow, needed human repair, produced accepted work, or changed a business result.
03How does AI agent observability support ROI measurement?
It connects total run cost and human intervention to accepted units of work and a defined business baseline. That makes cost per accepted outcome and process improvement visible without attributing every business change to the agent alone.
Start with who is already at work
Give every agent a place in the operating model.
Map the agents, owners, access, activity, cost, and outcomes already taking shape across the company.