Follow the Building AI Agents with MCP learning path on AI Builders Network - a free, structured roadmap with curated resources and step-by-step progress tracking.
This path is for developers who want to build agents that complete real multi-step work, research, triage, data reconciliation, rather than impressive-but-fragile demos. It centers on the Model Context Protocol, the open standard for connecting agents to tools and data.
By the end you'll be able to: Judge when an agent beats a deterministic workflow (and when not); Design tool interfaces models use correctly under pressure; Build and ship your own MCP server for a real system; Orchestrate multi-agent patterns with explicit state and routing; Apply agent safety: least privilege, approval gates, sandboxing.
MCP is an open standard, originally released by Anthropic and now industry-adopted, that lets AI models connect to tools, files, databases, and APIs through one protocol instead of custom integrations per app. Think USB for AI: write one MCP server for your system and any MCP-capable model client can use it.
Use a workflow when the steps are known in advance: it is cheaper, faster, and predictable. Reserve agents for tasks where the path genuinely varies by input, like open-ended research or debugging. The most common production mistake in 2026 is shipping an agent where a five-step workflow would do.
Bound the blast radius: give agents least-privilege tools, require human approval for irreversible actions, sandbox code execution, and treat all retrieved content as untrusted input to resist prompt injection. Then measure task completion rates with full-task evals: a safe agent that cannot finish work is still a failed product.