What "Multi-Agent" Actually Means

A single LLM call has a fixed context window, one perspective, and sequential reasoning. A multi-agent system distributes work across several model instances—each with its own role, context, tools, and memory—connected by an orchestration layer that routes messages and aggregates outputs. The minimum viable multi-agent setup has three parts: 1. Orchestrator — decides which agent runs next and what input it gets 2. Agents — specialized workers with scoped system prompts and tool access 3. Shared state — a data structure (usually a typed dict or message bus) all agents read from and write to Everything else—voting, debate, reflection loops—is built on top of these primitives.

Evaluating Multi-Agent Quality

You can't eyeball five agents across fifty runs. You need evals. - Per-agent evals: Each agent should have its own unit test suite with representative inputs and expected outputs. Test agents in isolation before wiring the graph. - End-to-end evals: Run the full pipeline on a golden dataset. Score outputs with an LLM judge or human rubric. Track regressions across framework upgrades. - Failure injection: Intentionally break one agent (return malformed output) and verify the orchestrator handles it gracefully. LLM-as-judge patterns work here—use a separate evaluator model to score agent outputs on correctness, relevance, and format compliance. Just don't use the same model that produced the output as the judge.

Pick one pattern (start with hierarchical) and build a minimal three-agent system around a real task you already do manually. - Add LangSmith or Langfuse tracing before anything else—you'll thank yourself on the first failed run. - Read [AI Model Context Protocol (MCP) 2026](/en/rehberler/ai-mcp-2026) to understand how tool access works across agent boundaries. - Once you have a working pipeline, apply the SRE lens from [AI for Backend Developers 2026](/en/rehberler/ai-backend-developers-2026) to harden it for production traffic.

AI Multi-Agent

AI Multi-Agent Systems 2026 TL;DR. AI multi-agent systems coordinate multiple specialized LLM instances to tackle tasks too large or complex for a single prompt. The patterns are now well-understood, the frameworks are maturing, but most teams still wire these up wrong—burning tokens, hitting rate limits, and producing worse results than a well-structured single-agent flow. This guide maps the ar…

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