Designing Resilient Agent Swarms

We've all seen it: a single AI agent gets confused, enters a loop, or hallucinates an answer. In a mission-critical workflow, this is unacceptable.

The solution is not a "smarter" single model. The solution is Swarm Architecture.

What is a Swarm?

A swarm is a collection of specialized agents working together. Instead of one "General Manager" trying to do everything, you typically have a team of narrow experts.

• The Router: Decides which agent should handle the user's request.
• The Specialist: Executes a specific task (e.g., "Write Python Code" or "Search Twitter").
• The Reviewer: Checks the Specialist's output for errors or safety violations.

Self-Healing Workflows

If the Reviewer rejects the work, it sends feedback back to the Specialist to try again. This loop continues until quality standards are met—all without user intervention.

Example: The Coding Swarm

1. User Request: "Build a React navbar."
2. Architect Agent: Breaks it down into files (Component, CSS, Tests).
3. Coder Agent: Writes the code.
4. Reviewer Agent: Runs the linter. If errors found -> Send back to Coder.
5. Final Output: Clean, linted code delivered to the user.

1# Pseudocode for a Reviewer Loop
2def review_loop(code, max_retries=3):
3    for i in range(max_retries):
4        lint_errors = linter.check(code)
5        if not lint_errors:
6            return code
7        
8        # Feedback loop
9        code = coder_agent.fix(code, lint_errors)
10    
11    raise Exception("Could not fix code after max retries")

Redundancy and Voting

For high-stakes decisions (like financial transactions), we use Voting Swarms. Three independent agents analyze the data. Action is taken only if at least 2 out of 3 agree.

This effectively eliminates random hallucinations. It's the same principle as reliable distributed systems: consensus protects against individual node failure.