Building a Deep Research Agent Using MCP-Agent
2 days ago
- #MCP-Agent
- #Deep Research
- #AI Development
- Sarmad Qadri shares his journey building a general-purpose deep research agent powered by MCP, emphasizing lessons learned.
- The agent's philosophy: 'MCP is all you need,' connecting state-of-the-art LLMs to MCP servers for tool calls and decision-making.
- Initial goal: Build an open-source agent for deep research and multi-step workflows using MCP tools.
- First attempt (Orchestrator): Used a planner LLM to break tasks into sub-tasks but faced issues like hallucination and token inefficiency.
- Second attempt (Adaptive Workflow): Introduced dynamic subagents, external memory, and budget management but struggled with navigation and performance.
- Third attempt (Deep Orchestrator): Simplified architecture, added deterministic verification, and improved memory usage, leading to better performance.
- Key learnings: Simple architecture wins, MCP is sufficient, and small details matter in agent development.
- Future plans: Remote execution, intelligent tool selection, memory as MCP resources, and dynamic model selection.