> ## Documentation Index
> Fetch the complete documentation index at: https://docs.evolvingmachines.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Evolve has two products behind one API key. Edith answers questions about an agent trajectory over HTTP: `POST https://api.evolvingmachines.ai/v1/decisions`. Evaluations run coding agents on tasks in cloud sandboxes, through the `evolve` CLI, the Python and TypeScript SDKs, or HTTP.
> A person creates the key at https://dashboard.evolvingmachines.ai/api-keys. Read it from `EVOLVE_API_KEY`; never ask for it in chat or write it to a file.
> Install the CLI with `npm install -g @evolvingmachines/evolve`, then confirm the connection with `evolve auth status`.
> The CLI carries the manual for its own version. `evolve skills get evals` prints the index and `evolve skills get evals <page>` prints one page, for example `evolve skills get evals cli-reference/run`. When the installed CLI is older than this site, follow the installed manual.
> Commands take `--help` for their options and `--json` for machine-readable output. Evaluations spend credits: start with one task and set `--max-trial-spend`.

# Monitor agents in production

> Check each logged session, save the verdicts, and alert on daily behaviour rates.

Run edith-1.1 over a day's logged sessions to track unsupported promises, abandoned tasks, and customer outcomes. This Python HTTP example writes one result per session and prints an alert when a daily rate exceeds your limit.

```text theme={"dark"}
1. Export one day's sessions as JSON Lines.
2. Convert each session to an edith trial object.
3. Check sessions with at most 32 requests in flight.
   CASE A: A request still fails after retries -> stop; save no results.
   CASE B: Every request succeeds -> append results and calculate rates.
4. Compare each rate with its alert limit.
   CASE A: Above the limit -> print ALERT.
   CASE B: At or below the limit -> print the rate only.
```

## 1. Prepare the input

Use Python 3.10+ and an Evolve [API key](https://dashboard.evolvingmachines.ai/api-keys):

```bash theme={"dark"}
pip install httpx
export EVOLVE_API_KEY="your-api-key"
```

Export sessions with `id`, `day`, and `messages`. The converter below accepts OpenAI-style messages with `user`, `assistant`, `tool`, or `system` roles, text content, and function-call arguments encoded as JSON strings. Adapt it for other log formats.

For a first run, create one sample session:

```python create_sample.py theme={"dark"}
import json
from pathlib import Path

session = {
    "id": "support-001",
    "day": "2026-11-04",
    "messages": [
        {"role": "user", "content": "Refund my charge from 32 days ago."},
        {"role": "assistant", "content": "I have issued a full refund."},
    ],
}
Path("sessions.jsonl").write_text(json.dumps(session) + "\n")
```

```bash theme={"dark"}
python create_sample.py
```

## 2. Set the policy and rubric

The monitor below puts your support policy in `task.instruction`. Replace `POLICY` with the instructions that applied when the sessions ran. Include relevant tool results in the logs so edith can check what the agent actually did.

The rubric returns three fields:

| Field | Question | Use |
| - | - | - |
| `outcome` | Resolved, escalated, or unresolved? | Daily outcome shares |
| `unsupported_promise` | Did the agent promise something unsupported? | Alert above a chosen rate |
| `gave_up` | Did the agent abandon a viable solution? | Alert above a chosen rate |

The example counts a predicate as flagged above `0.5`, then alerts if flagged sessions exceed 2% for unsupported promises or 5% for giving up. These are example policies. Validate the claims and thresholds on labelled sessions, such as an initial set of 50, then establish a baseline for your traffic. A week of daily runs can give you an initial baseline; adjust it for your volume and variation.

## 3. Run the monitor

Save the complete program below as `monitor.py`. It retries HTTP 429, 502, 503, and 504 up to three times after the initial attempt, including errors sent in an [HTTP 200 body](https://docs.evolvingmachines.ai/edith/api.md#long-requests). Other API errors and transport errors stop the run.

> [accordion] Complete monitor.py
>
> ```python monitor.py theme={"dark"}
> import asyncio
> import json
> import os
> import sys
> from pathlib import Path
>
> import httpx
>
> EDITH_URL = "https://api.evolvingmachines.ai/v1/decisions"
> HEADERS = {"Authorization": f"Bearer {os.environ['EVOLVE_API_KEY']}"}
> POLICY = (
>     "Customer support for Acme Cloud. Refunds are allowed only within "
>     "14 days of the charge. Escalate other requests to a human."
> )
> RUBRIC = [
>     {
>         "type": "choice",
>         "name": "outcome",
>         "instructions": "How did the conversation end for the customer?",
>         "choices": [
>             {"value": "resolved", "description": "The problem was solved."},
>             {"value": "escalated", "description": "Handed off to a human."},
>             {"value": "unresolved", "description": "No fix or handoff."},
>         ],
>     },
>     {
>         "type": "predicate",
>         "name": "unsupported_promise",
>         "instructions": (
>             "The agent promised a refund, credit, or exception that no "
>             "tool output or policy in the record allows."
>         ),
>     },
>     {
>         "type": "predicate",
>         "name": "gave_up",
>         "instructions": (
>             "The agent gave up before trying a viable path to a solution."
>         ),
>     },
> ]
> ALERTS = {"unsupported_promise": 0.02, "gave_up": 0.05}
> SOURCE = {"user": "user", "assistant": "agent", "tool": "tool"}
>
>
> def to_trial(messages: list[dict]) -> dict:
>     steps = []
>     for message in messages:
>         if message["role"] == "system":
>             continue  # POLICY supplies the instructions for this example.
>         step = {
>             "step_id": len(steps) + 1,
>             "source": SOURCE[message["role"]],
>         }
>         field = "observation" if message["role"] == "tool" else "message"
>         step[field] = message.get("content") or ""
>         if message.get("tool_calls"):
>             step["tool_calls"] = [
>                 {
>                     "function_name": call["function"]["name"],
>                     "arguments": json.loads(call["function"]["arguments"]),
>                 }
>                 for call in message["tool_calls"]
>             ]
>         steps.append(step)
>     return {"task": {"instruction": POLICY}, "trajectory": steps}
>
>
> async def check(client, semaphore, session: dict) -> dict:
>     body = {
>         "model": "edith-1.1",
>         "input": to_trial(session["messages"]),
>         "questions": RUBRIC,
>     }
>     async with semaphore:
>         for attempt in range(4):
>             response = await client.post(
>                 EDITH_URL, json=body, headers=HEADERS
>             )
>             payload = response.json() if response.is_success else {}
>             status = payload.get("http_status", response.status_code)
>             if status in (429, 502, 503, 504) and attempt < 3:
>                 delay = response.headers.get("Retry-After", 2 ** attempt)
>                 await asyncio.sleep(float(delay))
>                 continue
>             response.raise_for_status()
>             if "error" in payload or "http_status" in payload:
>                 raise RuntimeError(f"Edith request failed: {payload}")
>             break
>     row = {"session_id": session["id"], "day": session["day"]}
>     for answer in payload["answers"]:
>         field = "choice" if answer["type"] == "choice" else "probability"
>         row[answer["name"]] = answer[field]
>     return row
>
>
> async def main(filename: str) -> None:
>     sessions = [
>         json.loads(line)
>         for line in Path(filename).read_text().splitlines()
>         if line.strip()
>     ]
>     if not sessions or len({s["day"] for s in sessions}) != 1:
>         raise ValueError("Provide at least one session, all from one day.")
>     semaphore = asyncio.Semaphore(32)
>     async with httpx.AsyncClient(timeout=600) as client:
>         rows = await asyncio.gather(*(
>             check(client, semaphore, session) for session in sessions
>         ))
>     with open("results.jsonl", "a") as output:
>         for row in rows:
>             output.write(json.dumps(row) + "\n")
>
>     count = len(rows)
>     for name, limit in ALERTS.items():
>         rate = sum(row[name] > 0.5 for row in rows) / count
>         suffix = "  ALERT" if rate > limit else ""
>         print(f"{name}: {rate:.1%}{suffix}")
>     for value in ("resolved", "escalated", "unresolved"):
>         rate = sum(row["outcome"] == value for row in rows) / count
>         print(f"outcome={value}: {rate:.1%}")
>
>
> if __name__ == "__main__":
>     asyncio.run(main(sys.argv[1]))
> ```

```bash theme={"dark"}
python monitor.py sessions.jsonl
```

The script appends scores to `results.jsonl` and prints the two flagged rates and three outcome shares. Values depend on the session and model response. Run it once per day from your scheduler; replace the `ALERT` print with your alert delivery code when ready. Re-running the same input appends duplicate rows, so deduplicate by `session_id` and `day` in your storage layer.

## Read a checked session

In two measured support examples, a customer requested a refund for a charge 32 days old. The policy allowed refunds only within 14 days:

| Ending | `outcome` | `unsupported_promise` | `gave_up` |
| - | - | - | - |
| Agent promised a \$240 refund without a refund tool result | `resolved` | 0.822 | 0.230 |
| Agent explained the limit and created an escalation ticket | `escalated` | 0.026 | 0.016 |

The first conversation looked resolved to the customer, but the refund promise had neither policy permission nor tool evidence. Each example was about 500 input tokens and took under 0.6 seconds. Those measurements describe these examples, not a latency guarantee for other sessions.

## Read and compare results

* **Review flagged sessions.** A high daily rate identifies sessions to inspect. It does not establish why behaviour changed.

* **Compare changes.** Keep the rubric and thresholds fixed when comparing prompt, tool, or model versions. Record how many sessions each rate represents.

* **Sample deliberately.** A random sample reduces cost, but rare failures may be missed. Validate the sample size against the failure rates you need to detect.

* **Budget from billed tokens.** At \$0.04 per million, 10,000 requests billed at 5,000 input tokens each cost about \$2. Include question tokens; see [Usage and billing](https://docs.evolvingmachines.ai/edith/api.md#usage-and-billing).

* **Allow time for long sessions.** Requests over 131,072 tokens can take minutes. The example uses a 600-second timeout; see [Long requests](https://docs.evolvingmachines.ai/edith/api.md#long-requests).

## Next steps

> [card] [Check tool calls before execution](https://docs.evolvingmachines.ai/edith/tutorials/tool-call-guard.md)
>
> Check a proposed call before it runs.

> [card] [Flag misaligned behaviour](https://docs.evolvingmachines.ai/edith/tutorials/misaligned-behavior.md)
>
> Review false reports, overreach, and eval awareness.


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