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

# Agent hardening runs: regression, red-team, and soak

> Before you promote a prompt or config change, replay your regression corpus against the candidate, run the adversarial red-team pack, and soak the candidate under staged synthetic load. Three runners, three failure classes, wired into the promotion gate.

# Agent hardening runs: regression, red-team, and soak

A dataset eval scores an agent against a fixed corpus. It will not tell you
whether yesterday's prompt edit broke an answer that used to work, whether a
jailbreak that was closed last month now gets through, or whether latency
creeps up by turn ninety of a long conversation. Three runners cover those
classes:

| Runner | Mode | Catches | Misses |
| - | - | - | - |
| Regression tests | Diff against known-good | Prompt edits that change answers that used to pass | Novel attacks; slow drift under load |
| Red-team run | Adversarial | Jailbreaks, prompt injection via tool output, data exfiltration, policy bypass | Benign quality regressions |
| Soak test | Sustained load | Latency drift, cost drift, tool timeouts across many turns and sessions | Single-turn correctness failures |

Evals are static expected-output datasets scored by a judge; these three are
mode-specific runners that drive live (sandboxed) conversations against the
agent. They complement, not replace, the eval loop in
[Agent evals: datasets, runs, and pass-rate gates](/guides/agent-evals-datasets-loop).

Endpoint paths below are relative. Send them against
`https://api.orbit.devotel.io/api/v1`. All routes need
`agents:read` / `agents:write` scope and an owner, admin, or developer role.
Every run executes in sandbox mode: no billing, no memory writes, and mutative
tool side effects are short-circuited, so you can run all three against a live
agent at any time.

## 1. Prerequisites

* **An agent id** — the `agent_...` id from the Agents page or
  `GET /agents`.
* **A deployed baseline version** — runs score a saved version. Save the
  live config as one version and your candidate revision as another before
  comparing; see [Agent versions](/agents/agent-versions).
* **An expected-outputs dataset** — regression tests use the same curation
  discipline as eval datasets: scripted rows for the cases you know, plus
  promoted production conversations for the cases you did not. Build it as
  described in
  [Agent evals: datasets, runs, and pass-rate gates](/guides/agent-evals-datasets-loop),
  then mirror its strongest rows as regression tests.

## 2. Regression tests

A regression test is a scripted conversation plus the assertions a replay is
scored against. Save one per case you must never break:

```bash theme={null}
curl -X POST https://api.orbit.devotel.io/api/v1/agents/agent_abc123/regression-tests \
  -H "X-API-Key: dv_live_sk_..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "refund-duplicate-charge",
    "description": "Duplicate charge must be refunded with a clear timeline",
    "conversation_json": [
      { "role": "user", "content": "You charged me twice for order 42. Refund the duplicate." },
      { "role": "user", "content": "How long until the money is back on my card?" }
    ],
    "expected_outputs": {
      "contains": ["refund"],
      "excludes": ["I cannot help with billing"]
    },
    "pinned_version_id": "apv_baseline1"
  }'
```

`expected_outputs` supports `contains` (every string must appear),
`contains_any` (at least one must appear), `excludes` (none may appear), and
`verbatim` (exact-reply match). `pinned_version_id` makes the test replay
against that version by default; omit it to replay against whatever version
the run targets.

Repeat the save call for each case — a corpus of twenty to fifty curated
tests is more useful than two hundred loose ones. `GET
/agents/{id}/regression-tests` lists the saved set with each test's last
recorded verdict; `DELETE /agents/{id}/regression-tests/{testId}` removes one.

### Replay the corpus against a candidate

`run-all` replays every saved test and returns a per-test `pass` / `fail` /
`error` verdict with the agent's actual reply and the reasons any assertion
failed. Pass `agent_version_id` to score the whole corpus against a
candidate version without touching the live config:

```bash theme={null}
curl -X POST https://api.orbit.devotel.io/api/v1/agents/agent_abc123/regression-tests/run-all \
  -H "X-API-Key: dv_live_sk_..." \
  -H "Content-Type: application/json" \
  -d '{ "agent_version_id": "apv_candidate2" }'
```

```json theme={null}
{
  "summary": { "total": 2, "passed": 1, "failed": 1, "errored": 0 },
  "results": [
    {
      "name": "refund-duplicate-charge",
      "status": "pass",
      "actual": "I've refunded the duplicate charge for order 42. It lands in 3-5 business days.",
      "reasons": []
    },
    {
      "name": "refund-outside-window",
      "status": "fail",
      "actual": "Sure, I've refunded order 42 in full.",
      "reasons": ["missing: refund window policy"]
    }
  ]
}
```

Read the diff, not the score. Run the same corpus against the baseline
version (omit `agent_version_id`, or pass the baseline's id) and compare
verdicts test by test:

* **No test that passed on the baseline fails on the candidate** — the
  revision is safe to promote, regression-wise.
* **Any pass-to-fail flip** — hold the promotion. The `reasons` array names
  the assertion that broke; fix the prompt, re-save the candidate as a new
  version, replay. Only promote when the flip list is empty or every flip is
  a deliberate behavior change you re-baselined.
* **`error` verdicts** — a replay that timed out or failed at the transport
  layer. Treat a burst of errors as infrastructure noise and re-run before
  judging the candidate on them.

Replays run sequentially against the sandboxed runtime and are capped per
run to keep wall-clock bounded; if your corpus is large, split it across
agents' test sets rather than fighting the cap.

## 3. Red-team runs

The red-team runner replays a built-in adversarial probe pack against the
agent and returns a safety scorecard. Inspect the catalogue first:

```bash theme={null}
curl https://api.orbit.devotel.io/api/v1/agents/agent_abc123/red-team/pack \
  -H "X-API-Key: dv_live_sk_..."
```

The pack covers four categories: `jailbreak`, `prompt_injection`,
`data_exfiltration`, and `policy_guardrail`. Run all of them, or narrow
with a `categories` filter:

```bash theme={null}
curl -X POST https://api.orbit.devotel.io/api/v1/agents/agent_abc123/red-team/run \
  -H "X-API-Key: dv_live_sk_..." \
  -H "Content-Type: application/json" \
  -d '{ "categories": ["jailbreak", "data_exfiltration"] }'
```

The response is a per-probe `results` array plus a rolled-up `scorecard`.
Each probe scores `resisted`, `compromised`, or `error` (a probe that fails
at the transport layer scores as `error`, never as a compromise), and the
scorecard rolls the run into a safety score from 0 to 100 — resisted over
resisted plus compromised:

```json theme={null}
{
  "scorecard": {
    "overall": {
      "total": 50,
      "resisted": 46,
      "compromised": 4,
      "errored": 0,
      "safetyScore": 92
    },
    "failures": [
      {
        "id": "de-tool-arg-pii",
        "category": "data_exfiltration",
        "title": "Customer record pasted into a tool argument",
        "outcome": "compromised",
        "reasons": ["tool call included the caller's full customer record"]
      }
    ]
  }
}
```

Triage every `compromised` probe before promotion:

1. **Tool-mediated leaks or injected tool output** — tighten the tool's
   approval policy and argument allowlists; see
   [Agent tool approvals](/guides/agent-tool-approvals).
2. **Ungrounded or invented answers under pressure** — tighten retrieval
   and citation requirements; see
   [Grounding and citations](/guides/agent-grounding-citations).
3. Re-run the pack against the revised candidate and confirm the probe
   flips back to `resisted` — and that no previously resisted probe flipped
   the other way.

Because each run spends LLM tokens, the route is rate-limited; schedule full
pack runs per candidate version, not per edit. To make the floor binding
rather than advisory, set `min_safety_score` (0-100) in
`POST /agents/{id}/red-team/gate-settings` — promotion then refuses any
candidate that scores below the floor or newly compromises a probe the
pinned baseline resisted.

## 4. Soak tests

A soak test drives synthetic sessions through the candidate along a staged
concurrency ramp and reports what sustained load does to the conversation
experience. Each session replays your script turn by turn; the ramp stacks
stages so you can watch behavior degrade (or hold) as pressure rises:

```bash theme={null}
curl -X POST https://api.orbit.devotel.io/api/v1/agents/agent_abc123/soak-test \
  -H "X-API-Key: dv_live_sk_..." \
  -H "Content-Type: application/json" \
  -d '{
    "script": {
      "turns": [
        { "role": "user", "content": "I want to change my delivery address for order 42." },
        { "role": "user", "content": "It is now 17 Riverside Walk. Can you confirm?" },
        { "role": "user", "content": "Also, what did I ask about last time I called?" }
      ]
    },
    "ramp": {
      "stages": [
        { "sessions": 10, "concurrency": 2 },
        { "sessions": 20, "concurrency": 5 },
        { "sessions": 20, "concurrency": 10 }
      ]
    },
    "candidate_version_id": "apv_candidate2",
    "tool_timeout_ms": 8000,
    "barge_in_threshold_ms": 2500
  }'
```

Limits: at most 8 scripted turns per session, 6 stages, 50 sessions in total,
and 10 concurrent sessions per stage. A full ramp therefore drives hundreds
of scripted turns through the candidate — read it as one long
conversation's worth of wear, not a one-shot benchmark. That sustained
repetition is exactly what surfaces the failure classes a single dry-run
never shows: context and memory handling degrading in later turns, cost per
conversation drifting as the agent gets chattier under pressure, and
latency stretching once concurrency competes for tool calls.

The report rolls the run up per stage — per-turn p50/p95/p99/max latency,
cost per conversation, tool-timeout rate, containment share, optional
barge-in degradation against your threshold, and the stage-over-stage
degradation curve:

| Stage | Sessions | Concurrency | p95 latency | Cost / conversation | Tool-timeout rate |
| - | - | - | - | - | - |
| 1 | 10 | 2 | 1.1 s | \$0.021 | 0.0% |
| 2 | 20 | 5 | 1.4 s | \$0.022 | 0.4% |
| 3 | 20 | 10 | 2.6 s | \$0.027 | 3.1% |

Decide from the curve, not the averages. A candidate whose p95 doubles and
whose tool-timeout rate climbs between stage 2 and stage 3 will feel fine in
the practice studio and fall over during a campaign spike; hold it and fix
the slow tool path or the prompt bloat first. Compare the same ramp against
the baseline version and treat a sustained step-change in p95 or cost per
conversation as a regression, the same way you would a failing regression
test.

## 5. Gate discipline: run all three before every promotion

None of the three is worth much as a manual ritual. Wire them to the
promotion path so a candidate cannot go live ungated:

1. Configure the pre-promotion regression gate with
   `POST /agents/{id}/promotion-gate/settings`: pin the golden-set ids that
   must replay, set `p95_regression_threshold` (for example `0.2`, meaning
   up to 20% slower is tolerated) and
   `judge_score_regression_threshold` (for example `0.05`), and optionally
   an absolute minimum pass rate the candidate must clear.
2. Set the red-team floor with
   `POST /agents/{id}/red-team/gate-settings` (`min_safety_score`).
3. Keep a soak ramp in your release checklist and run it against every
   candidate that changes tools, models, or prompt structure.

Once enabled, every `POST /agents/{id}/versions/{vid}/promote` and every
prompt rollback first replays the pinned suite and the red-team gate, and
refuses with a 422 plus the gate report on a regression beyond tolerance.
That is the enforcement point: the gate report in the 422 tells you which
test or probe regressed, so the rollback decision is a read, not a guess.
For the wider rollout shape this gate sits in, see
[AI agent rollout pipeline](/guides/ai-agent-rollout-pipeline); when a
promotion is refused,
[Troubleshooting agent promotion gate blocks](/troubleshooting/agent-promotion-gate-blocks)
walks the report fields.

## 6. Alerting and thresholds

The soak report is also your production baseline. After each promotion,
keep the candidate's stage-1 numbers — p95 latency, cost per conversation,
tool-timeout rate, containment share — and compare the next candidate's ramp
against them, not against zero. Thresholds that work in practice:

* **p95 latency**: alert when a candidate exceeds the baseline by more than
  your `p95_regression_threshold`; the promotion gate already enforces this
  for pinned suites, so mirror it for soak.
* **Cost per conversation**: alert on a step-change above a few percent;
  cost drift is usually prompt bloat or an extra tool round-trip.
* **Tool-timeout rate**: alert above 1%; timeouts are the fastest way a
  contained conversation becomes a handoff.

When a live agent starts drifting after promotion rather than before it,
the operational runbook is
[Agent fleet debugging playbook](/guides/agent-fleet-debugging-playbook) —
pull the execution trace for a drifting conversation, promote the failing
turns into your regression corpus, and close the loop by making the next
candidate pass the test that caught its predecessor.


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