Archive

Ollama as the default LLM for enterprise-shaped systems

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.

Tool Use

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.

step23 · Local MCP (wrapping remote tools)

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.

Five Microsoft Agent Framework orchestration shapes —...

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.

The Core Agent Loop

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.

step21 · Foundry Web Search (hosted tool + citations)

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.

Four orchestration patterns in Microsoft Agent Framework...

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.

What an AI Agent Is

"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.

step14 · Code Interpreter (hosted tool)

This lesson teaches the hosted tool: a marker that lets the Foundry service run code the model writes, instead of a Go function you implement.

Attach hostedtool.CodeInterpreter, a zero-value marker with no Run method, so Foundry executes model-written Python in a sandbox to solve sin(x) + x^2 = 42.

The 12-chapter reference architecture, in 102 Python files

Microsoft published a 12-chapter reference architecture for multi-agent systems and a separate framework — the Microsoft Agent Framework — to build them. Here is what the 102 Python files actually contain and how they map to the chapters.

Data Engineering in Practice

All the components — pipelines, warehouses, models, batch and streaming, the modern stack, quality and governance — come together in a single job: keep reliable, usable data flowing to the people and systems that need it. Doing that in the real world is less about any one technology than about a mindset: treating data pipelines as production software that must be reliable, tested, observed, and maintained. This closing post is about data engineering as it's actually practiced, and where it's heading as AI makes good data more valuable than ever.

All the components come together in one job: keep reliable, usable data flowing to the people and systems that need it. Doing that in the real world is less about any one technology than about a mindset — treating data pipelines as production software that must be reliable, tested, observed, and maintained. Plus where the field is heading as AI makes good data more valuable than ever.

step13 · Plugins (grouping tools)

This lesson teaches how to bundle related function tools onto a Go type and attach the whole group to an agent as one slice.

Bundle GetWeather and GetCurrentTime onto one Go type, expose them as a tool.Tool slice, and let the model call both in a single turn — plugins as an organizing idea.