February 22, 2026
An agent without a workflow pattern is a chatbot. What separates an agent from a single-turn LLM call is the loop: observe the environment, reason about what to do, act, observe the result, and decide whether to continue. The loop structure determines everything – how the agent plans, how it recovers from errors, when it stops, and whether it can handle tasks that take minutes or hours.
February 22, 2026
The most common failure mode in agent-driven work is not a wrong answer – it is a correct action taken without permission. An agent that deletes a file to “clean up,” force-pushes a branch to “fix history,” or restarts a service to “apply changes” can cause more damage in one unauthorized action than a dozen wrong answers.
Human-in-the-loop design is not about limiting agent capability. It is about matching autonomy to risk. Safe, reversible actions should proceed without interruption. Dangerous, irreversible actions should require explicit approval. The challenge is building this classification into the workflow without turning every action into a confirmation dialog.
February 22, 2026
This reference covers building MCP servers from scratch – the server lifecycle, defining tools with proper JSON Schema, exposing resources, choosing transports, handling errors, and testing the result. If you want to understand when to use MCP versus alternatives, see the companion article on MCP Server Patterns. This article focuses on how to build one.
An MCP server goes through four phases: initialization, capability negotiation, operation, and shutdown.
February 22, 2026
A single agent can read files, call APIs, and reason about results. But some tasks are too broad, too slow, or too dangerous for one agent to handle alone. Debugging a production outage might require one agent analyzing logs, another checking infrastructure state, and a third reviewing recent deployments – simultaneously. Multi-agent coordination is how you split work across agents without them stepping on each other.
The hard part is not spawning multiple agents. The hard part is deciding which coordination pattern fits the task, how agents share information, and what happens when they disagree.
February 22, 2026
An agent with access to 30 tools is not automatically more capable than one with 5. What matters is how it selects, sequences, and validates tool use. Poor tool use wastes tokens, introduces latency, and produces wrong results that look right.
When multiple tools could handle a task, the agent must pick the best one. This is harder than it sounds because tool descriptions are imperfect and tasks are ambiguous.
February 21, 2026
Model Context Protocol (MCP) is Anthropic’s open standard for connecting AI agents to external tools and data. Instead of every agent framework inventing its own tool integration format, MCP provides a single protocol that any agent can speak.
An agent that supports MCP can discover tools at runtime, understand their inputs and outputs, and invoke them – without hardcoded integration code for each tool.
An MCP server exposes three types of capabilities: