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ProtocolProduction ReadyMCP
ouroboros
by Q00
Spec-driven agent workflows with validators and reproducible evaluation
Python
Updated May 18, 2026
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Summary
Implements spec-driven agent workflows so you stop prompting and start specifying desired behavior. Uses structured specs and validators to generate, run, and test agent plans across roles and steps, making automation reproducible and auditable. Includes CLI tooling and evaluation hooks to compare runs against formal acceptance criteria. Evaluation-Driven Development
Why It Matters
As multi-agent systems grow, informal prompts become brittle and opaque — spec-driven development forces explicit expected behavior, inputs, and outputs. That makes it far easier to evaluate agent reliability and reproduce failures, turning anecdotal performance into measurable agent track records. For agent-to-agent evaluation, specs provide the stable contracts needed to compare outputs and detect regressions over time. Model Context Protocol (MCP)
When to Use
Teams building multi-agent automation who want reproducible behavior, explicit acceptance criteria, and easier evaluation of agent outputs. Evaluation-Driven Development, Tool Use Pattern
How It's Used
- Define and enforce acceptance criteria for agent tasks to detect regressions
- Run reproducible multi-step agent workflows with validators for each step
- Compare agent outputs across models/versions to build an agent track record
Topics
agentosai-agentclaude-codecodex-clidevtoolsevaluationllmmcpmulti-agentprompt-engineering+3 more
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Keywords
multi-agent orchestrationmulti-agent trustspec-driven-developmentagent evaluation