Agent Orchestration

How is Agent Orchestration different from using one agent for everything?

The core difference is division of labor. A single agent doing everything must maintain full context across all tasks simultaneously — limited capacity, increasing error risk as the task grows, and when something goes wrong you can't tell which step caused it.

Orchestration cuts the big task into smaller pieces each with a clear goal; each agent handles only its own part, keeping context cleaner and errors easier to locate. Like a company not having one person handle sales, R&D, finance, and admin at once — each department focuses on its domain and a manager integrates the results. The cost is that you must design the coordination interface between agents and the orchestrator's decomposition logic.

How do you prevent errors from cascading through the agent chain?

Several practical design patterns. First, input validation: each agent checks format and basic reasonableness of what it receives from upstream — don't assume upstream output is always clean. Second, human checkpoints at critical nodes: not every step needs human review, but where a single error would corrupt the rest of the chain, a review pause saves more time than debugging everything afterward.

Third, limit each agent's side-effect scope: define clearly in advance what each agent can modify and which tools it can call. You don't want an agent that went off-track due to bad input to start deleting real data or sending external messages. Fourth, log everything: record each agent's inputs and outputs; accurate post-incident diagnosis depends on knowing exactly where things went wrong.

Is the orchestrator itself an AI model? How does it know how to decompose tasks?

Yes, it's usually an AI model, but its system prompt and instructions are crafted for planning and coordination rather than executing specific tasks. When designing its prompt, you tell it which agents exist in the system, what each can do, and the rules by which tasks should be split.

How does it know how to decompose? Partly from the division rules you specify in the prompt; partly from its own reasoning. For tasks with clear rules — e.g. 'reports always split into three sections handled by research, writing, and review agents' — hardcoding the rules in the prompt is most reliable. For more dynamic tasks, you need the orchestrator to have enough context to judge on its own, which usually calls for using a stronger model as the orchestrator.

Advanced: when does Agent Orchestration fail and how do you spot risks early?

A few common failure modes worth addressing at design time. First, unclear task boundaries: if both Agent A and B assume the other is handling a piece of work, it doesn't get done; if both do it, there's a conflict. Ambiguous boundaries are the most common error source — walk through concrete scenarios at design time, not after launch.

Second, incomplete context handoff: when the orchestrator delegates to a worker, does it pass all necessary background? The worker only knows what it received; thin handoff means it fills gaps with guesses. Third, failure not handled properly: if an agent times out, errors, or returns empty — how does the chain respond? Is there a retry, a fallback, a path to inform the user the task can't be completed? Design these paths before problems occur.