PieBox
Nos RéalisationsFonctionnalitésAvisDocsBlog
Se connecterEssayer PieBox
Retour au Blog

Building an AI Coding Agent: 5 Key Design Decisions

May 20, 2026PieBox Team

Why Build an AI Coding Agent?

The landscape of developer tools is shifting. GitHub Copilot proved that AI can autocomplete code. But we asked a different question: what if AI could own the entire development workflow?

Not just write code, but understand the project structure, run tests, fix bugs, deploy — all while maintaining context across a long-running session.

Decision 1: Tool-First Architecture

Instead of building a monolithic model that "knows everything," we designed PieBox around a tool-calling architecture. The AI agent has access to a well-defined set of tools:

  • File system operations (read, write, search)
  • Shell command execution
  • Browser automation
  • Knowledge retrieval
interface Tool {
  name: string
  description: string
  parameters: z.ZodSchema
  execute: (params: unknown) => Promise<ToolResult>
}

This means the model doesn't need to hallucinate file contents — it reads them. It doesn't guess command output — it runs them.

Decision 2: Streaming-First Execution

Every operation in PieBox streams results back to the user in real-time. This isn't just a UX choice — it's architectural:

  • Users can interrupt long-running operations
  • Context builds incrementally
  • Failures surface immediately rather than accumulating

Decision 3: Structured Context Management

The biggest challenge in AI coding isn't generating code — it's maintaining context. A real project has hundreds of files, complex dependency graphs, and implicit conventions.

We solve this with:

  1. AGENTS.md — project-level instructions that persist across sessions
  2. Explore tool — intelligent codebase search that finds relevant files
  3. Conversation compression — keeping the most relevant context in the window

Decision 4: Fail Fast, Fix Fast

Our agent follows a strict loop:

  1. Write code
  2. Run tests / typecheck immediately
  3. If failure → fix (max 2 rounds)
  4. If still failing → stop and report

This prevents the common failure mode of AI agents: silently generating broken code and moving on.

Decision 5: Delegate to Specialists

Not every problem should be solved by a general-purpose model. PieBox routes specific tasks to specialized sub-agents:

  • UI work → UI Engineer (design-aware, component library knowledge)
  • Architecture decisions → Advisor (sees full context, gives decisive answers)
  • Codebase exploration → Explorer (fast, cheap, read-only)

This mirrors how human teams work — specialists handle what they're best at.

What's Next

We're continuing to push the boundaries of what an AI coding agent can do autonomously. The goal isn't to replace developers — it's to give every developer the leverage of a 10-person team.

Stay tuned for more deep dives into our architecture.

aiagentarchitecture