I tried running three AI agents simultaneously once. The research agent found information. The writing agent drafted content based on that information. The review agent checked the draft for accuracy. In theory: a beautiful pipeline. In practice: the research agent found irrelevant information, the writing agent turned it into a confident but wrong article, and the review agent approved it because the claims were internally consistent — just disconnected from reality.
That was six months ago. I’ve since built multi-agent workflows that actually work. The difference isn’t the technology — it’s understanding when multiple agents help and when they make things worse.
When Multiple Agents Make Sense
Multi-agent workflows work when you have genuinely distinct tasks that benefit from specialization and can be clearly separated.
Good example: A code review pipeline. Agent 1 analyzes code for security vulnerabilities (specialized prompt, security-focused). Agent 2 checks code style and best practices (different specialized prompt). Agent 3 summarizes both reviews into a human-readable format. Each agent has a clear, narrow job. The outputs don’t conflict because they’re looking at different aspects.
Bad example: Three agents collaborating on writing an email. Agent 1 drafts. Agent 2 edits. Agent 3 reviews. In practice, Agent 2 undoes Agent 1’s tone choices, and Agent 3 often contradicts Agent 2’s edits. You end up with a mediocre average of three different writing styles. A single agent with a good prompt writes better emails.
The rule I follow: use multiple agents when the tasks are parallel (different perspectives on the same input) or clearly sequential with well-defined handoff points (Agent 1’s output is a complete artifact that Agent 2 can evaluate independently). Don’t use multiple agents when the tasks overlap or when “collaboration” means “arguing about style.”
My Working Multi-Agent Setups
Research → Summarize → Distribute. Agent 1 searches the web for information on a topic and compiles raw findings. Agent 2 takes those findings and creates a structured summary. Agent 3 formats the summary for different platforms (Slack post, email newsletter, internal wiki page). Each agent has a clearly defined input and output, and they don’t need to interact with each other — they’re a pipeline, not a committee.
Monitor → Analyze → Alert. Agent 1 monitors systems and collects metrics every 5 minutes. Agent 2 receives the metrics and analyzes them for anomalies (comparing to historical baselines). Agent 3 takes any anomalies and drafts alert messages with context and recommended actions. This works because each step produces a clearly defined output that the next step can consume without ambiguity.
Code → Test → Review. When I spawn a coding sub-agent to implement a feature, a second agent reviews the output — checking for bugs, style issues, and correctness. The key: the review agent only sees the final code, not the coding agent’s reasoning. This prevents the review agent from being influenced by the first agent’s explanations and forces it to evaluate the code on its own merits.
The Orchestration Patterns
Sequential pipeline. Agent A → Agent B → Agent C. Each agent takes the previous agent’s output as input. Simplest to build, easiest to debug, most predictable. This is where you should start.
Fan-out / fan-in. One task gets sent to multiple agents simultaneously (fan-out), then their outputs are combined (fan-in). Good for getting multiple perspectives: send the same code to a security agent, a performance agent, and a style agent, then combine their reviews.
Router pattern. An orchestrator agent looks at the incoming request, decides which specialized agent should handle it, and routes accordingly. “Is this a technical question? Route to the technical agent. Is it a billing question? Route to the billing agent.” Good for customer-facing systems with diverse request types.
Human-in-the-loop. Agent does work → human reviews → agent continues or revises. This isn’t “multi-agent” in the traditional sense, but it’s the most reliable pattern. The human provides the judgment and oversight that agents lack.
The Failure Modes
Compounding errors. Agent A makes a small mistake. Agent B builds on Agent A’s output without questioning it. Agent C further amplifies the error. By the end of the pipeline, the output is confidently wrong. Fix: add validation steps between agents, or have a final review agent that checks the output against the original input.
Lost context. When Agent A passes a summary to Agent B, information gets lost. Agent B works with an incomplete picture and makes decisions that Agent A’s full context would have prevented. Fix: pass structured data (key facts, not summaries) between agents, and include the original input alongside processed output.
Agent conflicts. Two agents that both modify the same output can conflict — one adds a section, the other removes it. Fix: clearly define which agent owns which aspects of the output. Don’t let multiple agents edit the same artifact.
Debugging difficulty. When a multi-agent workflow produces wrong output, figuring out which agent made the mistake is hard. Each agent’s output looks reasonable in isolation. Fix: log every inter-agent communication with timestamps and content. When something goes wrong, trace the pipeline step by step.
Start Simple
If you haven’t built a multi-agent workflow before, start with a two-agent sequential pipeline. Agent 1 does work, Agent 2 reviews it. That’s it. Get comfortable with the orchestration, the handoffs, and the debugging before adding more agents.
The best multi-agent workflows I’ve built have 2-3 agents. The worst had 5. More agents means more coordination overhead, more failure points, and more debugging complexity. The goal isn’t to have the most agents — it’s to have the right number of agents for the task.
🕒 Last updated: · Originally published: January 11, 2026