The AI Agent Harness is the foundational infrastructure that wraps around an LLM to transform it from a stateless reasoning engine into a reliable, autonomous, and production-ready agent. MeshFlow acts as your pre-built Agent Harness with 12 core pillars.
The Engine (Execution Loops): Handled by MeshFlow's Team and StateGraph run loops.
Delegation (Topologies): Handled by hierarchical teams and Team(pattern="supervised") where a planner agent spawns worker agents.
Context Management: Handled by Context Compaction strategies.
Tools (External Actions): The meshflow.tools registry exposes strictly-typed external functions.
Skills (Internal Cognitive Abilities): Semantic functions that teach the agent how to think, via the skills=[] parameter.
MCP (Model Context Protocol): Standardized injection of context and APIs.
Session Persistence: Accomplished via ReplayLedger.
Memory Architecture: Agent memory components, RAG modules, and Cross-Run Learning (CORAL).
Error Recovery: Automatic reflection and circuit breakers on execution timeouts.
Lifecycle Hooks: Human-in-the-loop (HITL) checkpoints.
Guardrails & Permissions: PII scrubbers and strict CostCaps.
Observability: OpenTelemetry integration and SOC2/EU AI Act audit reporting.