Every article tagged AI Agents, in one place.
Showing 14 articles
"Agent" is used for many things, but the core idea is simple: an AI agent is an LLM that decides which tools to call, sees the results, and keeps going until the task is done . Your code runs the tools; the model chooses what to do next. The loop Send the user...
Before MCP, every AI application needed custom glue code for every integration: one plugin for GitHub in app A, a different one in app B. The Model Context Protocol (MCP) is an open standard, introduced by Anthropic, that defines one way for AI applications to...
The model chooses tools based only on their names, descriptions and schemas. A vague description is the most common reason an agent picks the wrong tool or invents arguments. Write descriptions like documentation for a new teammate { "name": "create_refund", "...
The manual agent loop is always the same: call the model, run the requested tools, send results back, repeat. The Anthropic SDKs include a tool runner (currently in beta) that does this for you, with type-checked tool inputs. TypeScript with Zod import Anthrop...
When a request needs several independent lookups ("compare the weather in Delhi, Mumbai and Bengaluru"), the model can return multiple tool_use blocks in a single response. Running them one after another wastes time. Execute in parallel const toolUses = respon...
Reading data is low-risk. Sending emails, moving money or deleting records is not. A human-in-the-loop step pauses the agent and asks for approval before risky tools run. Classify your tools Read-only (search, get_*): run automatically. Reversible writes (draf...
An agent can only do what its tools allow. Good guardrails assume the model will sometimes make mistakes or be manipulated, and limit the damage when it does. 1. Least privilege Give each agent only the tools its job needs. Scope credentials: a read-only datab...
Every turn of an agent adds to the conversation: tool calls, large tool results, the model's replies. Long tasks can grow past the context window, and even before that, a bloated context makes the model slower, more expensive and less focused. 1. Keep tool res...
Agents are exciting, but they cost more, are slower and are harder to test than fixed code. Many features are better built as workflows : LLM calls arranged in a flow that your code controls. Start with the simplest thing One LLM call - classification, summari...
MCP connects models to real systems, so it inherits real security risks. Some apply to server authors, some to anyone installing servers. Risks for users and host apps Malicious servers. A local stdio server runs with your user's permissions. Only install serv...
Switching branches means stashing work, waiting for rebuilds and losing your place. A worktree is an extra working directory attached to the same repository, so different branches can be checked out at the same time. Basic commands # Check out an existing bran...
Agents are non-deterministic, so traditional "input X gives exactly output Y" tests only go so far. A solid testing strategy combines normal tests for your code with task-level evaluations of the agent's behaviour. 1. Unit-test the tools Tools are ordinary fun...
If an MCP server is reachable over HTTP, the Claude API can connect to it for you. You list the server in the request and the API handles tool discovery and tool calls, so there's no MCP client or agent loop in your code. The request The MCP connector is a bet...
LLM features fail in new ways: answers get worse without any error, costs spike because a prompt grew, an agent loops through tools. Normal API monitoring doesn't catch these, so add LLM-specific telemetry. 1. Usage and cost const response = await client.messa...
