Learn how to design robust multi-agent systems using agent chaining, enabling you to manage complex processes with specialised agents.
If you’ve previously created one or two basic agents, you are familiar with the enchanting sensation of having an agent that can do tedious jobs and save you time. But what if you’d like to take things a step further?
Agent chaining can help with it. Imagine a professional kitchen rather than a single, enormous “do everything” agency. The pastry chef handles dessert, the prep cook cuts vegetables and prepares batches of ingredients, the line cook uses those to create final dishes, and the expediter ensures everything is completed on schedule. Although each position is unique, they work together to provide a smooth eating experience.
Similar to this, agent chaining creates a coordinated sequence that may do considerably more than any single agent operating alone by having one agent complete its duty and deliver the result to the next.
The Significance of Agent Chaining
Many company procedures are not one-and-done. They include the following stages:
- Collect information- Make it tidy- Examine it- Share the outcomes
- Write up a meeting transcript. Write a summary of your notes. Make tasks out of action items.
- Create an outline for your blog. Extend into parts. Create a polished post.
You will soon encounter complications if you attempt to fit all of that into a single agent run. However, you may divide the process into manageable parts by chaining agents together, with each agent doing its best function before passing it on to the next.
The Operation of Agent Chaining
The crucial point is that you cannot operate an all-encompassing “orchestrator” agent that has perpetual control over everything. Use agent chaining instead, which functions as follows:
- Agent A finishes an assignment. For instance, it may produce a dataset that has been cleaned up.
- Agent B is triggered by Agent A. It transmits its output as input.
- Agent B completes its assignment. It may generate a report after analysing the dataset.
- Next steps are optional. Agent C may subsequently receive the report and convert it, for example, into a client-facing presentation.
Although each agent operates independently, when combined, they form a chain that yields a more complex workflow.
What Agent Chaining Doesn’t
It’s important to stress that agent chaining does not entail having a single “all-knowing” orchestrator that assigns tasks to sub-agents while working for hours.
Rather, consider agent chaining as passing the baton to the next runner after each agent has completed its leg in a relay race.
A Business Example: Insights from Customer Feedback
Let’s examine a real-world use case: providing your product team with insights from unfiltered client input.
Agent 1: Collect: Gathers comments from support tickets or recent survey replies.
Agent 2: Organise: Sorts feedback by theme, cleans up the wording, and eliminates duplicates.
Agent 3: Summarise: Produce a one-page synopsis for the product team that highlights the most important patterns.
Agent 4: Share: Emails the summary to stakeholders or posts it on Slack. By chaining agents, you can.
Best Practices for Chaining Agents
1) Maintain each agent’s attention.
Avoid giving an agent too many responsibilities. Handoffs are cleaner and less error-prone when duties are narrow and clearly defined.
As a general rule, your prompt should generally be divided into many agents if it contains a lot of “and then…” instructions.
Aim for one consistent output and one or two distinct inputs.
2) Carefully prepare handoffs.
The efficiency with which each stage transmits information to the next determines the chain’s quality.
Establish output formats: Be clear about the fields that should be returned and use standard forms like JSON or bullet lists.
Reduce ambiguity: When the subsequent agent anticipates a structured output, steer clear of outputs that are too lengthy or free-form.
Add references: When working with files or big databases, send IDs or URLs rather than the actual data.
3) Steer clear of needless nesting.
Connect your agents in a straightforward order rather than creating a single, massive “master” agent that calls every other agent.
Sequential triggers: After Agent A finishes, start Agent B, and then start Agent C.
Branch sparingly: If you must execute many tasks concurrently, use a summarising agent to bring the results back together and keep the logic simple.
Keep it simple: Adding extra orchestration levels makes it more difficult to troubleshoot and adhere to schedule constraints.
4) Step-by-step test the chain.
Don’t wait till the entire chain is put together to test its functionality.
Utilise test inputs: To validate each agent separately, save a few typical instances.
Verify contracts: Verify that each agent delivers precisely the anticipated keys, types, and formats after it has executed.
Measure run times: Keep track of how long each step takes. If one begins pushing a high runtime, divide or simplify. It may need to be simplified since it is too complicated.
5) Design for failure.
Things will occasionally go wrong—a data source might be unavailable, or an agent might return incomplete results.
Standardise 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.
6) Effectively manage the state.
Transmitting large text blobs between agents can lead to mistakes and is inefficient.
Pass references rather than unprocessed data: Once significant artefacts are stored, transfer links or IDs.
Whenever feasible, summarise: The majority of downstream agents do not require the complete original file, merely a digest or structured output.
7) Make iterations and versions.
Make it simple to enhance your agents as they change over time.
Keep track of prompt versions: Include a version number or label so you can identify the configuration that led to a certain outcome.
One thing at a time: When fine-tuning, make little adjustments to prompts or settings to see what functions best.
8) Place people at important checkpoints.
Not all chains should operate entirely without human intervention. Add human review before results are released for projects with greater stakes.
Steps for approval: Add review locations, such as customer-facing communications, where errors might be expensive.
Make reviews effective: Give reviewers the source context and structured output so they may promptly approve or make corrections.
Why Agent Chaining Should Excite You
Chaining is the next step after creating a basic agent. It enables you to manage more intricate, multi-step processes and integrate specialised agents to create something more than the sum of its parts. You may begin creating your own little ecosystems of cooperative agents with agent chaining.
Additionally, you don’t need to be a coder to execute any of it; all you need to do is construct your chain well.
Scaling from one-time assistants to coordinated systems of agents is accomplished through agent chaining. You may create significantly more effective workflows by segmenting large operations into smaller phases.
