Orchestration

An AI agent is a system that uses a language model to perceive its environment, make decisions, and take actions to reach a goal. Unlike a simple chatbot that only responds to prompts, an agent can loop: observe state, plan next steps, call tools, review results, and adapt until the task is done. Agents are built from several components. A planner breaks a goal into subtasks. A memory module stores conversation history and working context. A tool interface lets the agent call APIs, run code, query databases, or interact with other systems. A feedback loop checks whether each step moved the agent closer to the goal. Simple agents might answer a question by searching the web. Complex agents can write and test code, file pull requests, or coordinate with other agents. The more autonomy an agent has, the more important safety guardrails become, such as human approval for destructive actions and clear logging for every decision.

Design and orchestrate complex multi-tool workflows using the Model Context Protocol (MCP). Build intelligent agent systems that coordinate multiple MCP servers for sophisticated automation tasks.

Design long-horizon autonomous workflows where AI agents work reliably across extended timeframes to complete complex, multi-step tasks with minimal supervision.

Official Firecrawl MCP Server - Adds powerful web scraping and search to Cursor, Claude and any other LLM clients.

A system where multiple agents collaborate, compete, or delegate tasks to achieve complex goals.