Workflow design coordinates multi-step agent operations with defined execution order and error handling. Articles cover Microsoft Agent Framework workflow patterns, sequential and parallel composition, and the orchestration strategies that keep complex multi-agent workflows observable and fault-tolerant.
14 posts tagged with workflows. ← All posts
Durable workflows in Go: checkpoint and rehydrate, pause on a RequestPort for a human, nest sub-workflows, and coordinate through scoped shared state.
Durable MAF workflows in Go: checkpoint and rehydrate a fresh graph, pause on a RequestPort for a human, nest sub-workflows, and coordinate via scoped shared state.
Agents as graph executors in Go: switch routing, fan-out/fan-in barriers, and mixing typed function nodes with agent nodes.
Agents as Go workflow executors: switch routing, fan-out and fan-in barriers, and mixing typed function nodes with agent nodes in one graph.
The graph model underneath every multi-agent app: executors as nodes, edges as data flow, and typed events streaming out of `WatchStream` as it runs.
The MAF workflow model in Go: executors bound to IDs, AddEdge wiring, WithOutputFrom, and typed WatchStream events - plus an upstream route-builder fix.
Durable workflows in Python: checkpoint and resume, pause for a human with request_info, and package a workflow as an agent.
Durable MAF workflows in Python: checkpoint and resume every superstep, suspend on request_info for a human decision, and package a workflow as an agent.
Agents as graph nodes: switch-case routing, fan-out/fan-in, and mixing plain functions with agent steps in one workflow.
Agents are just workflow executors: switch-case routing, fan-out/fan-in concurrency, and mixing plain function nodes with agent nodes in one graph.
The graph model underneath every multi-agent app: executors as nodes, edges as data flow, and typed events streaming out as it runs.
The MAF workflow model in Python: executors as nodes, edges as data flow, switch-case routing, and typed streaming events - learned model-free.
How two Foundry agents cooperate inside a cyclic graph workflow where one of them owns the loop control.
Two Foundry agents cooperate in a cyclic workflow: a SloganWriter drafts, a FeedbackProvider critiques and owns loop control via YieldOutput vs SendMessage.
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.
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.
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.
The other primitive: a directed graph of executors wired by edges, running fully offline with no model, no credential, no Foundry.
Wire two executors into an uppercase to reverse pipeline with the fluent workflow builder and run it fully offline via inproc.Default, iterating ExecutorCompletedEvents.
The functional API hid the graph. Here it's explicit: executors are nodes, edges join them, and a message flows from the start executor along the edges.
Build a two-node graph with WorkflowBuilder: executors send_message or yield_output, and the type hint on WorkflowContext carries the routing intent.
A workflow is not an LLM thing — it's a way to compose steps with checkpointing and events. This one has no agent and no model call, so it runs with zero credentials.
A credential-free workflow of two @step functions composed by a @workflow, isolating checkpointing, steps and outputs from any model call.
Compose several agents into a pipeline with the gentlest workflow API: decorate an async function with @workflow and call agents inside it with plain await.
Decorate an async function with @workflow and call agents inside it with plain await: a writer drafts, an editor tightens, one line of glue each.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.