1046 posts · Page 69 of 88. ← Blog
A provider whose "credential" is a local process, gated by a human-in-the-loop permission handler for every action the model wants to take.
A provider whose credential is a local copilot CLI process, with an OnPermissionRequest handler that approves or rejects each shell action the model wants to run.
The Microsoft Agent Framework deliberately does not ship an agent registry. Here is why that is the right call, and what to build as a project-local convention when you need one.
Ask an agent to "research this market and write a report" and it faces the same problem a person would: the task is too big to do in one leap. The answer, for agents as for people, is to break it down — decompose the goal into steps, and work through them. Planning is how agents handle complexity that the basic reason-act loop alone would fumble, and the patterns for doing it — from planning upfront to decomposing on the fly — are among the most important in agent design.
Ask an agent to 'research this market and write a report' and it faces the same problem a person would: the task is too big to do in one leap. The answer, for agents as for people, is to break it down. Planning is how agents handle complexity that the basic reason-act loop alone would fumble.
How swapping in the Gemini provider changes the constructor and credential — and nothing else about your agent.
The Joker agent, unchanged, now backed by Google Gemini: geminiprovider.NewAgent takes a genai.Client and an API key instead of an Azure token credential.
PROVIDER=ollama, granite4.1:3b, zero API keys, no Azure account. How to make a multi-agent project that demonstrates enterprise patterns without requiring enterprise infrastructure.
An LLM on its own can only do one thing: generate text. It can't search the web, run code, query a database, check the current time, or send a message — it can only produce words. Tools are what break that confinement, turning a model that can only talk into an agent that can act. Tool use is arguably the single most important capability that makes agents possible, and understanding how it works — and how to design tools well — is central to building effective agents.
An LLM on its own can only do one thing: generate text. Tools are what break that confinement, turning a model that can only talk into an agent that can act. Tool use is arguably the single most important capability that makes agents possible.
This lesson teaches how to connect to a remote MCP server and decorate the tools it exposes with your own local behavior before an agent uses them.
List a remote MCP server's tools, then wrap each FuncTool with a logging decorator via embedding plus one Call override — MCP tools compose like any tool.FuncTool.
The first ten posts treated the Microsoft Agent Framework as having four orchestration patterns. The official docs say five. Here are the two I missed — Group Chat and Magentic — and why they matter.
Strip away the frameworks, the tooling, and the jargon, and every LLM agent reduces to one simple loop: think about what to do, do it, look at what happened, repeat. This reason-act-observe cycle — crystallized by the ReAct pattern — is the beating heart of every agent, and understanding it deeply is understanding agents themselves. Once you see the loop clearly, agent frameworks stop being mysterious: they're all just implementations of this same fundamental cycle.
Strip away the frameworks and every LLM agent reduces to one simple loop: think about what to do, do it, look at what happened, repeat. This reason-act-observe cycle — crystallized by the ReAct pattern — is the beating heart of every agent, and understanding it deeply is understanding agents themselves.
This lesson teaches how to attach the hosted web-search tool and read the citation annotations the service returns with its answer.
Attach hostedtool.WebSearch so Foundry grounds its answer on live results, then pull CitationAnnotations off content headers with a pure, unit-testable extractor.
Sequential, Concurrent, Handoff, and Custom WorkflowBuilder. Four shapes the Microsoft Agent Framework ships out of the box, when to pick each, and the gotchas that cost me a day.
"Agent" has become one of the most overused and least precise words in AI — applied to everything from a chatbot with a system prompt to a fully autonomous system that writes and ships code. Cutting through the hype requires a clear definition: an agent is a system where an LLM decides its own actions in a loop, using tools, until a goal is met. That one distinction — the model choosing what to do next, rather than following a fixed script — is what separates a genuine agent from a workflow, and it's where both the power and the difficulty come from.
'Agent' has become one of the most overused words in AI. Cutting through the hype requires a clear definition: an agent is a system where an LLM decides its own actions in a loop, using tools, until a goal is met. That one distinction — the model choosing what to do next, rather than following a fixed script — is where both the power and the difficulty come from.