Orchestration coordinates the execution order and data flow between agents, services, or workflow steps. Posts cover Microsoft Agent Framework workflow orchestration, ADK-to-Microsoft Agent Framework migration patterns, Magentic-One orchestration, and the design patterns that keep complex multi-step workflows reliable and observable.
15 posts tagged with orchestration. ← All posts
Lesson 5 of Harness Engineering in Go — a triage step that first-matches a keyword and hands the request to a specialist, and the exact place a substring table stops being able to think.
Lesson 5: a triage router first-matches a keyword to hand intent to a specialist, and the exact point a substring table stops being able to think.
The prebuilt orchestration builders in agent-framework-go — Sequential, Concurrent, Group Chat — plus wrapping a whole workflow as one agent.
The Sequential, Concurrent, and Group Chat orchestration builders in agent-framework-go, plus wrapping a whole workflow as one nestable agent.
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.
Five prebuilt multi-agent shapes — Sequential, Concurrent, Group Chat, Handoff, Magentic — and when each beats hand-wiring a graph.
Sequential, Concurrent, Group Chat, Handoff, Magentic — the five prebuilt MAF orchestrations in Python and when each beats hand-wiring a graph.
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.
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.
For open-ended tasks with no known solution path: a manager agent plans, keeps a shared ledger, and round-by-round picks which specialist acts next. Ordering is dynamic, not a fixed graph.
Magentic orchestration puts a manager agent in charge: it plans, keeps a shared ledger, and picks which specialist acts next round by round until it synthesizes an answer.
A collaborative conversation among agents, coordinated by an orchestrator that decides who speaks next. Agents share full history and refine each other's work over rounds — a star topology.
Group chat coordinates several agents in a star topology: an orchestrator decides who speaks next while agents share full history and refine each other over rounds.
How to port ADK's orchestration callbacks to Microsoft Agent Framework builders without losing control.
How to port ADK's orchestration callbacks to Microsoft Agent Framework builders without losing control. The executor pattern: you own the loop.
A mesh of specialists where any agent can transfer the whole conversation to a better-suited peer — no central orchestrator. Control passes agent-to-agent via an auto-injected handoff tool call.
Handoff orchestration lets any agent transfer the whole conversation to a better-suited peer via an auto-injected tool call. Run unattended with autonomous mode.
Run several agents on the same prompt in parallel, then fan their answers back in. `ConcurrentBuilder` wires the fan-out/fan-in graph — latency is max(agent), not sum.
Concurrent orchestration fans one prompt out to several agents in parallel, then fans their answers back in with a built-in aggregator. Latency is max(agent), not sum.
Wire agents into a pipeline where each runs in turn and feeds the next. Hand a list to `SequentialBuilder`, call `.build()`, run it once — draft then review, extract then summarize.
Sequential orchestration chains agents so each runs in turn and feeds the next. Hand a list to SequentialBuilder, build, and run once — draft then review.
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.