Agent Orchestration

Why Agent Orchestration Matters

A single AI agent handling one task is easy to reason about. The moment several agents work on connected steps and hand off to each other and to people, the hard question stops being whether the agent can do it and becomes whether the whole thing stays coherent, governed, and accountable. Orchestration is the answer to that question.

Imagine a hiring workflow where one agent sources candidates, another screens them, a third drafts outreach, and a recruiter approves before anything sends. Each step works in isolation. Orchestration is what makes sure the screening agent uses the sourcing agent's output, that the human sign-off sits at the right point, and that if one step fails the whole chain does not quietly keep producing garbage.

Teams tend to focus on making each agent smarter and underinvest in the seams between them. But distributed automated work fails at the handoffs, not usually in the middle of a task, which is why orchestration rather than raw model capability is what decides whether multi-agent work can be trusted. It is a governance problem as much as a technical one, and it sits close to the heart of talent intelligence workflows.

How Agent Orchestration Works

Orchestration is mostly about the connections between agents, not the agents themselves, and a few of those connections do the work. Sequencing sets what runs in what order and what each agent is handed, so the screening agent receives the sourcing agent's actual output rather than starting cold. Handoffs move that output cleanly between agents and to human checkpoints, which is where most failures actually happen, a step passes along a plausible but wrong result and the next agent builds on it. Governance keeps the whole chain auditable and inside policy, so you can see after the fact what each agent did and why.

The failure mode worth designing against is silent propagation: one agent produces something subtly wrong early, and because nothing checks it, the error compounds through every downstream step. Good orchestration puts validation and human review at the seams where a bad handoff would travel furthest, not evenly across every step.

Single Agent vs Orchestrated Agents

A single agent is a closed loop: one goal, one actor, one output to check. Orchestrated agents are a system, with the reliability questions any system carries, dependencies, handoffs, and points of failure. The practical implication is that orchestration needs the same discipline as workflow design: clear ownership, human checkpoints where the stakes are high, and visibility into what each agent did. Capability per agent is necessary, but coordination across them is what makes the result usable.