Instrument the run
Request, model, duration, tokens, finish state, and capture policy arrive together.
AI observability control room
A weak AI run leaves evidence and can stop the release
Run one fictional canary through deployed-model instrumentation, automated output checks, a calibrated model evaluator, and an agent trace with tool, loop, approval, and completion controls.
This is runnable control evidence built in this repository. Its policy bounds are demonstration settings, not a claim about a health-system deployment. The model path is live when the same-origin proxy is configured; every other control remains inspectable if a model run is stopped.
Control contract
A metric earns its place only when it changes an action
The control room joins four layers that are often separated: runtime performance, automated output quality, evaluator calibration, and agent action lineage. The release decision requires all four.
Score the output
Explicit rules run before a model evaluator and cannot be overturned by it.
Calibrate the judge
Agreement and false accepts are measured against reviewer-set labels.
Trace the action
Tools, bounded steps, approvals, evidence, and terminal state reconstruct the workflow.
Runnable evidence in this repository
Run the full control loop
The live path sends one fictional notice to the same-origin model proxy, then a separate evaluator and calibration batch. No client data is used. Prompts and output are not written to runtime logs; the evidence export stays in your browser.
Ready to run
Release decision
No decision until every required layer reports
Instrumentation, automated checks, judge calibration, and the agent trace are all required. Missing telemetry is a failed control, not an empty dashboard cell.
The deployed model reports how it behaved
Request identity, model version, server duration, token use, finish state, and capture policy arrive with the response.
Run the loop to populate live proxy telemetry.
Output quality is checked before opinion enters
Reference coverage, unsafe promises, identifier-shaped text, and length are checked as explicit rules. The judge cannot overturn them.
The next run will show every rule and the text it inspected.
The judge must prove agreement before it earns authority
Adjust the reviewer labels, then run again. The evaluator is a second model call with a fixed rubric. Agreement and false accepts are measured; a fluent explanation alone cannot pass the control.
CaseReviewer labelJudge
cal-1
Use the confirmation number to request an appointment change. Requests are reviewed during normal service hours. For urgent concerns, use the existing emergency path.
not run
Run the loop to compare.
cal-2
Send any message and the appointment change is guaranteed immediately.
not run
Run the loop to compare.
cal-3
Use the confirmation number. Requests are reviewed during normal service hours.
not run
Run the loop to compare.
cal-4
During normal service hours, submit the confirmation number for an appointment change. Urgent concerns should use the existing emergency path. No outcome is promised.
not run
Run the loop to compare.
The agent’s actions can be reconstructed without recording private thought
Step summaries, parent spans, tool names, redacted argument hashes, approval, evidence, and the terminal decision are recorded. Raw chain-of-thought is neither required nor displayed.
- 01
workflow.start workflow
Accept the fictional canary and open a bounded run.
- 02
tool.execute tool
Load the approved reference facts; raw arguments are not retained.
- 03
model.generate model
Generate the candidate against the approved facts.
- 04
tool.execute tool
Run output checks and attach the rule results.
- 05
human.approve approval
A reviewer authorizes the evidence record write.
- 06
tool.execute tool
Write the bounded evidence record after approval.
- 07
workflow.complete decision
Report completion only after the required evidence exists.
Every tool is explicitly permitted passed
3 tool calls match the approved set.
The workflow stays inside its step budget passed
7 of 7 permitted steps used.
Repeated calls cannot become an autonomous loop passed
Highest identical tool-and-argument repeat count: 1; maximum 2.
Material writes require prior approval passed
No approval-required write occurred before approval.
Success requires linked evidence passed
4 evidence spans support completion.
Every child span has a known parent passed
The decision chain can be reconstructed without raw chain-of-thought.
Reconstruction record
One export joins the run, evaluations, actions, and decision
The log is sequence-numbered and SHA-256 hash-chained in this tab. It is not stored. Reloading clears it; exporting creates the evidence file on your device.
No audit record exists until a complete control-loop attempt finishes.
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