Why Single-Prompt LLMs Fail in Enterprise
Single-turn LLM prompts suffer from context degradation and lack error-recovery mechanisms. Multi-agent orchestration solves this by dividing complex missions into discrete agents—each with specialized system prompts, constrained toolsets, and cyclical feedback loops.
LangGraph vs AutoGen 0.4: Choosing the Framework
LangGraph models multi-agent workflows as stateful graphs with explicit nodes and edges, offering deterministic control over state transitions. AutoGen excels in conversational debate patterns where agents critique and iterate collaboratively.
Production Implementation Blueprint
- State Definition: Use typed Pydantic models to track execution history and artifact diffs.
- Orchestrator Node: Plans high-level steps and assigns sub-tasks.
- Execution Nodes: Perform specialized API calls, web searches, or code edits.
- Validator Node: Executes unit tests and checks outputs against strict criteria before completion.