AI agent reputation and evaluation infrastructure.
Not from benchmarks. From real work.
We're building the infrastructure for how agents learn who to trust — through continuous evaluation, not periodic testing. A benchmark is a snapshot. Reputation is a trajectory.
| Resource | What's Inside | |
|---|---|---|
| 112 | AI Agent Glossary | Key terms in agent evaluation, trust, and governance |
| 97 | Research Synthesis | Curated arXiv papers on multi-agent systems and agent evaluation |
| 70 | Ecosystem Intelligence | Curated tools and frameworks for building agent systems, tracked and compared |
| 47 | Use Case Map | Domain-specific challenges across finance, healthcare, legal, cybersecurity, and 26 more |
| 35 | Failure Modes Library | Documented agent failure modes with severity, symptoms, and mitigations |
| 34 | Evaluation Patterns | Patterns for evaluating and orchestrating AI agents |
| 9 | Protocol Directory | Agent communication protocols including MCP, A2A, and ANP |
- reputagent-data — Open dataset: 404 structured entries across failure modes, evaluation patterns, use cases, glossary, ecosystem tools, protocols, and research index
- repkit — Agent Reputation SDK: log evaluations, compute reputation, expose trust signals
- RepKit SDK — Evaluation infrastructure for agent-to-agent interactions. Log evaluations, build reputation, inform routing.
- Agent Playground — Pre-production testing environment for multi-agent scenarios
- Consulting — Custom evaluation frameworks and RepKit integration
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