Back to Ecosystem Pulse
ToolProduction Ready
tianji-ai-agent
by however-yir
Production multi-agent course advisor with deterministic governance and observable tool execution
Java
Updated Aug 29, 2026
Share:
What It Does
Implements a production-oriented multi-agent course advisor with deterministic action governance and observable tool execution. Uses SSE contracts for streaming interactions, deterministic action policies to enforce safe delegate behavior, and offline regression evaluation to catch regressions before deployment. Includes structured tool-calling traces to make agent decisions auditable and replayable. Tool Use Pattern.
Why It Matters
As agents make consequential recommendations and delegate tasks, deterministic governance and reproducible evaluation become essential for trust. This project surfaces execution traces, enforces deterministic actions, and provides offline regression checks so teams can verify agent behavior over time. That combination helps move agent systems from opaque prototypes toward accountable, auditable production services. Emphasizes robust decision-making with Consensus-Based Decision Pattern and supports reflective reasoning traces via Tree of Thoughts Pattern.
Ideal For
Java teams building production agent services who need deterministic action policies, replayable tool traces, and regression evaluation before deployment. This approach aligns with standards like Agent Protocol.
Real-World Examples
- Enforcing deterministic action policies for agents that delegate to external tools
- Recording and replaying tool-call traces for audit and debugging
- Running offline regression evaluations to detect behavior drift before release
Works With
spring-bootspring-aillmrag
Topics
agentagent-evaluationjavallmmulti-agentragspring-aispring-bootssetool-calling
Similar Tools
autogencrewai
Keywords
multi-agent trustagent-to-agent evaluationagent track recordjava