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AgentFlow
by lupantech
Optimize and observe multi-agent workflows with runtime tuning and metrics
Python
Updated Feb 8, 2026
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Overview
Optimizes Dynamic Task Routing Pattern to manage multi-agent workflows and agent behavior in-stream to improve task outcomes. It Agent Protocol instruments agent interactions, applies reward/learning signals, and tunes delegation and tool use across agents. Notable features include configurable optimization loops, reinforcement-based policy updates, and metrics hooks for debugging and analysis.
Key Benefits
As agents coordinate and delegate, subtle failures and brittle behaviors emerge that static testing misses. Event-Driven Agent Pattern lets teams observe agent interactions in context and continuously optimize policies and routing decisions, turning runtime behavior into actionable signals. For trust and evaluation, that means you can close the gap between benchmark results and real-world agent reliability.
Ideal For
Teams building multi-agent systems who need continuous optimization, delegation tuning, and visibility into agent decision-making. See how the LLM-as-Judge Pattern can inform evaluation of agent choices and outcomes.
Real-World Examples
- Tune delegation policies when a director agent routes tasks to specialists
- Continuously optimize agent policies using reinforcement signals from live runs
- Log and analyze agent interactions to uncover failure modes before production
- Measure agent track record and aggregate per-agent performance metrics
Topics
agentic-aiagentic-systemsllmsllms-reasoningmulti-agent-systemsreinforcement-learningtool-augmented
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Keywords
multi-agent trustagent-to-agent evaluationmulti-agent orchestrationagent reliability