337 posts · Page 9 of 29. ← Blog
This lesson teaches how to chain three role-specialised agents — researcher → fact_checker → reporter — into one sequential workflow and stream each stage.
A sequential workflow of three role-specialised Foundry agents — researcher, fact_checker, reporter — built with NewSequentialWorkflowBuilder and streamed stage by stage.
This lesson teaches that orchestration is a property of the workflow, not the agents — the same three agents drop into three different built-in graph shapes.
The same three agents dropped into three built-in agentworkflow builders — sequential, concurrent, and round-robin group chat — showing orchestration is a property of the graph, not the agents.
Agents return a clean 200 OK and still be wrong, unsafe, or expensive. Why agentic AI needs a new observability layer — LLM-as-judge, safety metrics, and the four lifecycle stages.
This lesson teaches how to host real `agent.Agent`s as workflow executors and chain them into a sequential pipeline.
Host three translation agents as workflow executors and chain them French to Spanish to English, using DisableForwardIncomingMessages and a TurnToken to drive a strict pipeline.
OWASP Agentic Top 10 coverage with YAML policy files, two API surfaces, and a metric bridge that shows policy denials in Grafana.
This lesson teaches how to build a two-executor pipeline and watch it run as a live stream of typed events.
Your first Agent Framework Go workflow: two string executors wired by an edge, run with RunStreaming and watched as a stream of typed events, fully offline.
I built memory, communication, security, governance, and evals from scratch first. Then I deleted most of it and used the Microsoft Agent Framework-native packages. Here is the audit table and what survived.
Compose three kinds of Agent Skill — code-defined, struct-based, and file-based — into a single agent through one skills context provider.
One skills ContextProvider blending three origins: in-memory volume and temperature skills plus a file-based unit-converter, unified behind one tool surface for the model.
Single-turn evals check one decision. Multi-turn evals check the whole trajectory. A Python harness with three evaluators, an offline test suite, and the judge prompt that actually works.
An Agent Skill — on-demand instructions, resources, and scripts — defined entirely in Go, with no SKILL.md files on disk.
An Agent Skill built from Go closures: instructions, a static and a runtime-generated resource, and a convert script that runs in-process with no SKILL.md files.
OpenTelemetry through the Microsoft Agent Framework's configure_otel_providers, custom workflow spans, custom metrics for runs/duration/agent selection, Jaeger + Prometheus + Grafana wiring, and the set-once latch gotcha.
Teach an agent a capability from a folder of files — a SKILL.md manifest, resources, and scripts — loaded on demand via progressive disclosure.
Teach an agent from a SKILL.md manifest, resources, and scripts on disk: fsskills scans the tree and a skills ContextProvider exposes load, read, and run tools.