OpenRe was built to move agent development from demo quality to engineering quality. The core architecture ties together benchmark datasets, trace capture, safety policies, and optimization loops so system changes can be measured and audited.
Problem
Agent systems are easy to demo but hard to evaluate consistently. Without tracing and policy controls, teams cannot confidently compare variants or operate safely.
Constraints
- Benchmarks needed reproducible runs across multiple agent configurations.
- High-risk actions required explicit approval paths.
- Trace data had to be durable enough for audit and post-run analysis.
Architecture
- Orchestration runner that executes task datasets under versioned config inputs.
- Adapter model for tools and model providers to keep core workflow provider-agnostic.
- Safety policy engine with risk tiers and approval gates.
- Trace event pipeline feeding evaluation and optimization commands.
Tradeoffs and Failures
- Strict safety controls reduce incident risk but can slow experimentation velocity.
- Rich trace capture improves observability while increasing storage and analysis overhead.
- Adapter flexibility broadens ecosystem support but requires careful contract management.
Engineering Impact
- Established repeatable benchmark workflows instead of one-off prompt demos.
- Added auditable safety controls to multimodal agent operation flows.
- Enabled optimization loops based on trace and evaluation evidence.
Outcomes
- Better comparability between agent variants under shared datasets.
- Improved governance signal through immutable run artifacts and approval logs.
- Clear separation between orchestration logic, policy decisions, and reporting outputs.
What Made This Approach Different
OpenRe treats agent development as an operations discipline. The architecture is intentionally built around measurement, policy, and auditability, not just model capability.