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AI & Agents

Agent Orchestration

Agent orchestration is the layer that decides which AI agent handles which part of a task, in what order, and what happens when one fails. It covers routing, shared state, iteration and spend limits, and the verification step that decides whether a sub-result is accepted or retried.

Without explicit orchestration, multi-agent systems behave emergently — which reads as sophisticated in a demo and as unpredictable in production. Explicit control flow means a failed run can be replayed and reasoned about rather than re-run hopefully.

The two design choices that matter most are how agents communicate and where the limits live. Typed contracts between agents turn a whole class of failures into validation errors instead of confused behaviour, and hard caps on iterations and spend belong in the orchestrator rather than in any agent's prompt.

Codazz builds this in production — Multi-Agent Systems.

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Agent Orchestration
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It decomposes the goal into sub-tasks, routes each to the agent best suited to it, collects results, verifies them against the original intent, and decides whether to accept, retry or escalate. It also enforces the limits — maximum iterations, tool calls and spend — that stop a multi-agent system looping expensively.

Related terms

Multi-Agent System

A multi-agent system is an architecture where several specialised AI agents work on one goal, typically coordinated by a supervisor agent that decomposes the task, routes each part to the right specialist, and checks results before accepting them.

AI Agent

An AI agent is a software system that pursues a goal by taking actions, not just producing text.

Agent Evaluation

Agent evaluation is the practice of scoring an AI agent against a fixed set of real tasks with verified expected outcomes, so that every prompt change, model swap or tool update can be measured rather than guessed at.

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