Hello, I'm Rijul. I'm building git-lrc, a micro AI code reviewer that runs on every commit. It's free and source-available on GitHub. Star git-lrc to help more developers discover the project. Do give it a try and share your feedback
We often hear terms like AI agents and Agentic AI, but they're usually discussed in the singular, as if one agent is responsible for everything.
You can think of an AI agent like a person. An individual can accomplish impressive tasks on their own, but a team of people with different skills can solve much larger and more complex problems. The same idea applies to AI agents.
Instead of relying on a single agent, a multi-agent system brings together multiple specialized agents that collaborate toward a shared goal. One agent might research, another might write code, while another reviews the final output. By dividing responsibilities, the system becomes more capable, scalable, and easier to maintain.
Why Use Multiple Agents?
A single agent has to plan, reason, use tools, and generate the final response. As tasks become more complex, this quickly becomes difficult to manage.
By splitting work across multiple specialized agents, each one focuses on a specific responsibility. This makes the overall system easier to extend, maintain, and scale.
Common Architectures
Manager-Worker
Think of an engineering manager leading a team. The manager doesn't write every line of code. Instead, they understand the requirements, assign work to the right people, and combine the results.
A manager-worker architecture follows the same idea. A manager agent delegates tasks to specialized worker agents, each responsible for a specific job, before assembling the final response.
Sequential Pipeline
Imagine an assembly line in a factory. A product moves along a conveyor belt, with each station adding something before passing it to the next.
A sequential pipeline works the same way. Each agent performs one step of the workflow and hands its output to the next agent until the task is complete.
Research → Analysis → Writing → Review
Parallel Workers
Not every task depends on another. When work is independent, teams often tackle it simultaneously to save time.
Parallel worker architectures follow the same principle. Multiple agents work on different parts of a problem at the same time, and their outputs are merged once everyone finishes.
Debate
Think about an engineering design discussion. Multiple engineers propose different approaches, challenge each other's ideas, and eventually arrive at the best solution.
A debate architecture works similarly. Multiple agents generate different answers, critique each other's reasoning, and together refine the strongest response before presenting it to the user.
Shared Memory
For agents to collaborate effectively, they often need access to the same information.
Instead of every agent maintaining its own copy of data, they can read from and write to a shared memory.
This keeps every agent working with the same context and avoids duplicated information.
Routing
Not every request needs every agent.
A router decides which agent is best suited for a task and forwards the request accordingly.
User
│
▼
Router
├── Math Agent
├── Search Agent
└── Writing Agent
This reduces unnecessary work, lowers cost, and improves response time.
When Should You Use Multi-Agent Systems?
Multi-agent systems are a good fit when:
- A problem can be divided into specialized responsibilities.
- Different agents need access to different tools or data.
- Tasks can run in parallel.
- You want one or more agents to validate or review another agent's work.
For simpler applications, however, a single well-designed agent is often faster, cheaper, and easier to maintain.
As with any software architecture, the goal isn't to use more agents. It's to use the right number of agents for the problem you're solving.
AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.
Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.
Give it a ⭐ star on Github
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