How to Chain Multiple AI Agents Together

Why Chain Agents Instead of Using One

A single agent that tries to classify data, extract entities, generate a response, send a notification, and update a database all in one prompt is hard to debug, expensive to run, and fragile. If any part fails, the whole thing fails. If the classification logic needs to change, you have to rewrite the entire prompt and risk breaking the other parts.

Chaining solves this by giving each agent one job. The classification agent only classifies. The response generator only generates responses. Each agent can use a different AI model optimized for its specific task. The classification step might use GPT-5-nano (1 credit) because the decision is straightforward, while the response generator uses GPT-4.1-mini (4 credits) because it needs to produce polished text.

Chained agents are also easier to maintain. When you need to update how responses are generated, you modify only the response agent without touching the classification or notification logic. Each agent is small enough to understand and test independently.

Common Chaining Patterns

Sequential Pipeline

The simplest chain is a straight line: Agent A's output feeds into Agent B, which feeds into Agent C. Each agent runs after the previous one completes. A document processing pipeline might work like this: Agent A extracts text from uploaded files, Agent B classifies the document type, Agent C routes the document to the appropriate department and sends a notification.

Fan-Out Pattern

One agent's output triggers multiple downstream agents that run independently. A customer feedback agent classifies the feedback, then fans out to three separate agents: one that updates the analytics database, one that sends an internal alert if the feedback is negative, and one that generates a response to the customer. Each downstream agent operates on the same classification result but takes different actions.

Aggregation Pattern

Multiple agents each process different data sources, and their outputs feed into a single aggregation agent that combines the results. A daily intelligence agent might chain three source agents (one monitoring social media mentions, one scanning review sites, one checking support ticket trends) into a summary agent that produces a unified daily report. The summary agent uses conditional logic to highlight the most important findings.

Conditional Chain

The first agent classifies the input, and a conditional step routes to different downstream agents based on the classification. A lead processing chain might classify leads as hot, warm, or cold, then route hot leads to an immediate response agent, warm leads to a nurture sequence agent, and cold leads to a data enrichment agent. Each downstream agent is specialized for its specific lead type.

Passing Data Between Agents

In Chain Commands, data passes between steps through workflow variables. When Agent A (a workflow step using an AI call) produces output, that output is stored as a variable. Agent B (the next AI step in the workflow) receives that variable as part of its input.

Direct Variable Passing

The simplest method passes the previous step's output directly into the next step's prompt. The classification agent returns "SUPPORT" and the response agent receives "The customer's inquiry was classified as: SUPPORT. Generate an appropriate initial response." The variable is inserted into the prompt template automatically.

Structured Data Passing

For more complex chains, have each agent return structured data (JSON format). The first agent returns something like {"category": "support", "urgency": "high", "topic": "billing"}. The next agent receives all three fields and can use them independently. This is more reliable than passing free-text output because each downstream agent knows exactly where to find the data it needs.

Database-Mediated Passing

For chains that span different workflows or run at different times, agents communicate through the database. Agent A writes its results to a database record. Agent B, running later or in a separate workflow, reads that record and continues processing. This pattern is essential for chains where one agent runs on a schedule and another runs on demand, or where the chain spans hours or days.

Choosing Models for Each Agent in the Chain

One major advantage of chaining is that each agent can use a different AI model. The total cost of a chain is the sum of each agent's individual cost, so using cheap models where possible keeps the overall chain affordable.

• Classification agents: GPT-5-nano (about 1 credit). The decision is usually simple (choose from a fixed set of categories) and does not require sophisticated reasoning.
• Data extraction agents: GPT-4.1-mini (about 4 credits). Extracting specific fields from unstructured text requires good comprehension but not creative generation.
• Response generation agents: GPT-4.1-mini or Claude Sonnet (about 4-8 credits). Generating natural, professional responses benefits from a more capable model.
• Analysis and reasoning agents: GPT-4.1 or Claude Opus (about 15-30 credits). Complex analysis, multi-factor decisions, or tasks requiring deep reasoning justify a premium model.
• Summary agents: GPT-4.1-mini (about 4 credits). Summarizing information from multiple sources is a moderate task that mid-tier models handle well.

A three-agent chain using GPT-5-nano for classification (1 credit), GPT-4.1-mini for extraction (4 credits), and GPT-4.1-mini for response generation (4 credits) costs about 9 credits total. Doing all three tasks in a single GPT-4.1 call would cost about 15 credits and be harder to debug.

Handling Failures in a Chain

When one agent in a chain fails, you need to decide whether to retry, skip, or abort. The answer depends on which agent failed and how critical its output is to downstream agents.

Retry Logic

For transient failures (API timeout, rate limit), add a retry branch. The workflow attempts the AI call, checks for success, and retries once if it fails. Most transient errors resolve on the second attempt. Do not retry more than once or twice, as persistent failures indicate a real problem that retrying will not fix.

Fallback Agents

If the primary model for an agent is unavailable, route to a fallback agent using a different provider. If the Claude-based response agent fails, a fallback GPT-based response agent can handle the request. The output quality may differ slightly, but the chain continues rather than dropping the request entirely.

Partial Chain Completion

For chains where later agents are optional (like the analytics update in a customer service chain), mark those agents as non-critical. If a non-critical agent fails, log the error and continue with the remaining agents. The customer still gets their response even if the analytics write fails.

For critical chain failures (the classification agent fails, so downstream agents have no data to work with), add error handling that logs the failure and queues the input for reprocessing. A scheduled agent can periodically check for failed items and attempt to reprocess them.

Real-World Chaining Examples

Customer Service Pipeline

Agent 1 (GPT-5-nano, 1 credit): Classify the incoming message as support, sales, billing, or spam. Agent 2 (GPT-4.1-mini, 4 credits): For support messages, extract the product name, issue description, and urgency level. Agent 3 (GPT-4.1-mini, 4 credits): Generate a personalized response using the extracted details and the customer's account history from the database. Total cost: about 9 credits per message, with spam filtered out at step 1 for only 1 credit.

Content Moderation Pipeline

Agent 1 (GPT-5-nano, 1 credit): Quick screen for obvious violations (profanity, spam patterns). Agent 2 (GPT-4.1-mini, 4 credits): For borderline content, perform deeper analysis considering context, sarcasm, and platform rules. Agent 3 (conditional): If flagged, notify the moderation team with the AI's analysis and recommended action. Most content passes Agent 1 cleanly at 1 credit, with only borderline cases incurring the full 5-credit analysis. See content moderation agent for more detail.

Lead Processing Pipeline

Agent 1 (GPT-4.1-mini, 4 credits): Score the lead based on form data, source, and company size. Agent 2 (conditional): Route by score. Agent 3a (GPT-4.1-mini, 4 credits): For high-score leads, generate a personalized outreach message. Agent 3b (no AI): For low-score leads, add to drip campaign with no AI call needed. High-quality leads cost 8 credits, low-quality leads cost only 4.

Step-by-Step: Build a Chained Agent Pipeline