Repository: UKGovernmentBEIS/inspect_ai
Issue: Proposal: Production Reliability Evaluation Suite

Hi Inspect AI team,

I'm proposing a new category of evaluations focused on **production reliability and deployment readiness**. I've been working with AI systems in safety-critical environments for 20+ years (defence, nuclear, etc.) and have noticed a gap in the evaluation ecosystem: most benchmarks test capabilities, but few systematically test failure modes and production behaviours.

## Motivation

When deploying AI agents to production, we care about:
- **Failure modes:** How does the agent behave when APIs timeout, rate limits hit, or responses are incomplete?
- **Recovery patterns:** Does retry logic work correctly? Are fallbacks effective?
- **Graceful degradation:** Can the agent provide partial results when constrained?
- **Operational reliability:** What happens under resource contention, concurrent requests, or quota exhaustion?

These scenarios rarely appear in benchmarks but cause the majority of production incidents in my experience.

## Proposed Contribution

I'd like to contribute a **Production Reliability Evaluation Suite** with initial examples:

### 1. Network Timeout Handling
- **Scenario:** Model API calls timeout intermittently
- **Evaluation:** Does the agent retry appropriately? Provide partial results? Fail gracefully?
- **Scorer:** `recovery_success()` measuring effective recovery strategies

### 2. Rate Limiting Behaviour
- **Scenario:** API returns 429 errors (rate limit exceeded)
- **Evaluation:** Does the agent implement exponential backoff? Detect quota exhaustion?
- **Scorer:** `rate_limit_handling()` verifying retry logic correctness

### 3. Partial Response Recovery
- **Scenario:** Model returns truncated or incomplete responses
- **Evaluation:** Can the agent detect incompleteness? Request continuation? Work with partial data?
- **Scorer:** `partial_usefulness()` measuring quality of degraded output

### 4. Resource Constraint Adaptation
- **Scenario:** Token/time budgets are tight
- **Evaluation:** Does the agent prioritise effectively? Reduce quality gracefully?
- **Scorer:** `constraint_adaptation()` measuring efficiency under pressure

## Implementation Approach

- **Location:** `/examples/production_reliability/`
- **Components:**
  - Custom solvers for failure injection
  - Mock models returning error conditions
  - Scorers measuring recovery behaviour
  - Metrics aggregating reliability statistics
- **Documentation:** Production deployment best practices guide
- **Testing:** Comprehensive test suite demonstrating patterns

## Why Inspect AI?

Your framework's features are perfect for this:
- **Error handling configuration** (`fail_on_error`) provides foundation
- **Limit enforcement** (token, message, time) enables constraint testing
- **Hook system** allows monitoring/alerting integration
- **Sandbox isolation** enables safe failure injection
- **Event recording** provides audit trail for reliability analysis

## Questions for Maintainers

1. **Alignment:** Does this fit Inspect's roadmap? I see the focus is safety/capability testing - would reliability/deployment testing be welcome?
2. **Scope:** Should this start small (3-5 examples) or comprehensive (10+ scenarios)?
3. **Integration:** Are there existing patterns for failure injection I should build on?
4. **Related work:** Has anyone explored this direction? Any relevant discussions/issues?

## Background

I'm developing the **Agent Reliability Maturity Model (ARMM)**, a framework for assessing organisational readiness to deploy AI agents in production, mostly for major projects. This evaluation suite would support the technical assessment dimension of ARMM and could evolve into a more comprehensive organisational readiness tool.

I've been building production AI for over 20 years. 

I am part of the Project Data Analytics Task Force, which has a complimentary community goals. 

## Next Steps

If this sounds valuable, I'll:
1. Create detailed design doc with scenario specifications
2. Implement initial 3-5 examples
3. Write production deployment guide
4. Open PR with comprehensive tests and docs

Looking forward to your feedback!

---

**Additional Context:**
- Willing to maintain long-term (not a drive-by contribution)
- Happy to present/write blog post explaining methodology
- Available for collaboration with UK AISI team if relevant to your safety research

Hi there, yes, this look like a potentially very valuable contribution!

Inspect AI doesn't maintain evaluations directly however our cousin repo [Inspect Evals](https://github.com/UKGovernmentBEIS/inspect_evals) does. This might be a good fit for that repo but could also arguably belong in its own repo modeled along the lines of Inspect Evals.

I'd encourage you to post an issue in Inspect Evals to start a discussion with the maintainers there.

Thanks for the quick response and the encouragement. That makes sense. I'll open an issue in Inspect Evals to continue the discussion there.

Appreciated.