AI Agents
Focused ai agents articles with clear context, practical examples, source links where needed, and honest limits.
7 articles in this section.
AI evaluation gates for tool calls: test actions, not only answers
An agent can write a convincing final answer while calling the wrong tool or using the wrong arguments. Evaluate the action trace, permissions, and side...
AI agent background jobs: checkpoints, cancellation, and human handoff
Long-running agents need the same reliability ideas as workers: durable checkpoints, cancellation, idempotency, and an operator who can take over when t...
WebMCP explained: how websites may expose tools to AI agents
WebMCP points toward websites exposing structured actions for browser agents. Developers should think about tool contracts, permission boundaries, and u...
Model routing for AI agents: small model, frontier model, or fallback?
A multi-model agent should not send every request to the most expensive model. Route based on task risk, context size, latency, and the cost of being wrong.
Local AI agents on a laptop: a realistic privacy and speed guide
Local agents are appealing because code and documents can stay on the machine. They are not automatically private or fast, so the design needs clear boundaries
Google Gemini agent for work: what developers should learn from it
Google’s new workplace agent direction points toward systems that connect models to documents, inboxes, business tools, and developer environments. Developers c
Gemini managed agents explained: sandboxes, tools, and governance
Managed agents promise to handle longer tasks with tools and background execution. The important design question is what the agent may read, change, and approve