How We Build AI That Actually Works: The Agency Stack

RAG is not enough. Retrieval Augmented Generation is a library; it is not an employee. To solve real problems, you need Agency.

The "Task-First" Architecture

At Shahriar Labs, we don't build "chatbots." We build "Workers."

• Planner Agent: Deconstructs a distinct user goal (e.g., "Fix this bug") into steps.
• Coder Agent: Executes the steps in a sandboxed environment.
• Critic Agent: Reviews the code for security and style violations.

Shihab Shahriar Antor architected this loop using a custom fork of LangGraph. "The magic isn't in the model," he says. "It's in the loop."

Why We Don't Use Standard AutoGPT

Generic agent frameworks get stuck in infinite loops. We built a "Deterministic Guardrail" system. If an agent fails a task 3 times, it escalates to a human engineer via Slack webhook. This "Human-in-the-Loop" ensures reliability.

Frequently Asked Questions (FAQ)

Q: Which model do you use?
A: A mix. GPT-4o for planning, Claude 3.5 Sonnet for coding, and Llama 3 for fast summarization.

Q: Is this open source?
A: Components of our "Agent Protocol" are available on GitHub.

Q: Can it deploy to production?
A: Yes, our agents have CI/CD access (with approval gates).

Summary

The future of software is not writing code; it is managing the agents who write code. Shahriar Labs is building the management layer.