If you’ve already built a simple agent or two, you know the magical feeling of an agent that can take on repetitive tasks and save you time. But what if you want to go a step further?
That’s where agent chaining comes in. Instead of imagining one giant “do everything” agent, picture a professional kitchen. The prep cook chops vegetables and pre-makes batches of ingredients, the line cook takes those and makes finished dishes, the pastry chef handles dessert, and the expediter makes sure it all comes together on time. Each role is specialized, but together they create a seamless dining experience.
Agent chaining works the same way: One agent finishes its task and passes the output to the next, creating a coordinated sequence that can accomplish far more than any single agent working alone.
Many business processes aren’t one-and-done. They involve stages:
If you try to cram all of that into a single agent run, you’ll quickly hit complexity. But by chaining agents together, you can break the process into manageable steps—each agent doing what it does best, then handing off to the next.
Here’s the important part: You can’t run an overarching “orchestrator” agent that controls everything indefinitely. Instead, use agent chaining, which works like this:
Each agent runs independently, but together they form a chain that produces a more complex workflow.
It’s worth emphasizing that agent chaining doesn’t mean you have a single “all-knowing” orchestrator running for hours, delegating tasks to sub-agents.
Instead, think of agent chaining as handing the baton in a relay race: Each agent runs its leg, then passes the baton smoothly to the next runner.
Click here and give this agent a run. (If you're not in NYC, just pretend you are for the sake of learning.)
This might seem like a basic agent that simply looks up well-rated cocktails bars and museums within a certain radius of a given address. But what's actually happening is Agent A (night planning agent) gets an address as input, then calls Agent B (cocktail recommendations) and Agent C (museum recommendations). Those outputs are fed back into Agent A, which takes the research from the chained agents, synthesizes it, and outputs recommendations based on the combined data from Agents B and C.
With this setup, each agent can focus on doing one thing well, which makes the final output more reliable and of a higher quality. (Or to bring us back to our restaurant analogy ... you're drinking a cocktail made by a bartender, not a chef who's also menu planning, line cooking, doing dishes, and running the bar.)
Let’s walk through a practical use case: turning raw customer feedback into insights for your product team.
Each agent has a focused role, and by chaining them, you’ve built an end-to-end process that transforms messy inputs into actionable insights—without overwhelming a single agent run.
Don’t overload an agent with too many tasks. Narrow, well-defined jobs reduce errors and make handoffs cleaner.
Rule of thumb: If your prompt has a lot of “and then … ” instructions, it should probably be split into multiple agents.
Inputs/outputs: Aim for 1–2 clear inputs and one consistent output.
The quality of a chain depends on how well each step passes information to the next.
Define output formats: Use consistent structures like JSON or bullet lists, and be explicit about what fields should be returned.
Minimize ambiguity: Avoid outputs that are overly long or free-form when the next agent expects something structured.
Include references: If you’re working with files or large datasets, pass along IDs or links instead of the raw data itself.
Instead of building one large “master” agent that calls all the others, connect your agents in a simple sequence.
Sequential triggers: Let Agent A complete, then trigger Agent B, then Agent C.
Branch sparingly: If you need to run multiple steps in parallel, keep the logic lightweight and bring results back together with a summarizing agent.
Keep it simple: The more layers of orchestration you add, the harder it is to debug or stay within time limits.
Don’t wait until the whole chain is assembled to see if it works.
Use test inputs: Save a few representative examples to validate each agent individually.
Check contracts: After each agent runs, confirm that it returns exactly the expected keys, types, and formats.
Measure run times: Track how long each step takes, and split or simplify if one starts pushing a high runtime. It might be too complex, and need simplification.
Things will occasionally go wrong—a data source might be unavailable, or an agent might return incomplete results.
Standardize error messages: Make it easy to see which step failed, and why.
Allow simple retries: A quick re-run often resolves transient issues without manual intervention.
Passing massive text blobs between agents is inefficient and can create errors.
Pass references, not raw data: Store large artifacts once and hand off identifiers or links.
Summarize, when possible: Most downstream agents only need a digest or structured output, not the entire original file.
Your agents will evolve, so make it easy to improve them over time.
Track prompt versions: Add a version number or label so you know which configuration produced a given result.
Change one thing at a time: When refining, adjust prompts or settings incrementally to see what works best.
Not every chain should run 100% hands-off. For higher stakes tasks, add human review before results go out.
Approval steps: Insert review points where mistakes would be costly—like customer-facing communications.
Make reviews efficient: Provide reviewers with the structured output and source context so they can approve or correct quickly.
If you’ve already built a simple agent, chaining is the next level. It lets you handle more complex, multi-step workflows, and combine specialized agents into something bigger than the sum of its parts. With agent chaining, you can start to build your own mini ecosystems of agents that work together.
And you don’t need to be a developer to do any of it—you just need to be thoughtful about how you design your chain.
Agent chaining is how you scale from one-off helpers to coordinated systems of agents. By breaking big processes into smaller steps, you unlock much more powerful workflows.
To get started, look at a process you already automated with a single agent. Ask: “What’s the next logical step?” Then create a new agent to handle that step, and connect them. Congratulations—you’ve built your first agent chain.