Briefing
Microsoft built one of the first serious answers to a question every team building with AI eventually hits: what happens when one chatbot isn't enough, and you need several of them working together?
That answer is AutoGen (opens in a new tab), an open-source framework out of Microsoft Research for building applications where multiple conversational agents talk to each other, hand off work, and pull a human in when the job calls for it (Microsoft Research (opens in a new tab)). Picture a coder agent that writes a script and a reviewer agent that checks it, or a manager agent farming tasks out to a team of workers. AutoGen gives you the plumbing for that.
It landed early, and that matters. As one of the first multi-agent frameworks to come out of a major tech company, it reportedly helped set the terms for how a lot of people now think about agent orchestration. For an Australian business team weighing up whether to wire several AI agents together, it's worth understanding what AutoGen does well and where the ground has shifted under it.
One thing to know up front: as of 1 October 2025, Microsoft folded AutoGen and Semantic Kernel into a new Microsoft Agent Framework (opens in a new tab) (public preview), which is now the recommended path for production work. The concepts below still hold, but if you're starting fresh today, check where Microsoft is pointing people before you commit.
Conversational Agents as Core Primitives
AutoGen's basic unit is the conversational agent (opens in a new tab). Each one can:
- Send and receive messages
- Generate responses using LLMs
- Execute code in sandboxed environments
- Call tools and APIs
- Request human input when needed
The interesting part is how the agents interact. A conversation can be:
- Two-agent: a simple back-and-forth, like coder and reviewer
- Group chat: several agents debating and building consensus
- Hierarchical: manager agents handing work down to worker agents
- Custom: any topology you can define (opens in a new tab) with conversation patterns
Code Execution Built-In
AutoGen's standout feature is built-in code execution. The agents don't stop at writing code, they run it, read the output, and try again (AutoGen docs (opens in a new tab)). This runs in Docker containers for isolation, though the exact execution backend and how dependencies get handled depends on your version and config.
from autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent("coder", llm_config={"config_list": [...]})
user_proxy = UserProxyAgent("user", code_execution_config={"work_dir": "coding"})
user_proxy.initiate_chat(
assistant,
message="Write a Python script that plots the Fibonacci sequence"
)
# The assistant writes code, the proxy executes it, they iterateThat run-and-iterate loop is why AutoGen suits data analysis, scientific computing, and software work, where the actual deliverable is code.
Human-in-the-Loop
AutoGen keeps the human in the picture instead of designing them out. Agents can:
- Ask for clarification when the requirements are vague
- Request approval before running anything sensitive
- Present options when there's more than one sensible path
- Learn from feedback to do better next time
The thinking here is straightforward: fully autonomous agents aren't always what you want. Human judgement still earns its keep, especially when the stakes are high (AutoGen Human-in-the-Loop docs (opens in a new tab)).
Advanced Patterns
Nested Chats: an agent can spin off a sub-conversation to crack a sub-problem, which lets you stack problem-solving into hierarchies (opens in a new tab).
State Machines: spell out explicit state transitions for workflows that need tight process control.
Custom Agents: build specialised agents by subclassing the base classes and overriding behaviour.
Group Chat Managers: run multi-agent discussions with speaker-selection strategies you configure.
The Microsoft Ecosystem
AutoGen rides on Microsoft's research budget and plugs into Azure:
- Azure OpenAI: LLM access with enterprise guarantees
- Azure Container Instances: scalable environments for code execution
- Azure Cognitive Services: vision, speech, and search
- Semantic Kernel: a bridge into Microsoft's wider AI framework (opens in a new tab)
When to Choose AutoGen
AutoGen earns its place when:
- Code execution is a core requirement
- You want a human involved throughout
- You need complex conversation patterns
- Tying into the Microsoft ecosystem actually buys you something
The framework is mature, the docs are solid, and there's real research behind it. For enterprise teams building agentic apps, it offers a mix of capability and reliability that few alternatives match. Just keep one eye on the Microsoft Agent Framework, since that's where Microsoft is now steering production builds.
AutoGen: answer-first summary
AutoGen matters because it can change how Founders and operators plan, build, or govern an tool evaluation workflow. Microsoft's AutoGen framework enables complex multi-agent conversations with human participation, code execution, and flexible agent patterns.
The direct answer is this: do not treat the topic as a standalone trend. Treat it as a decision about inputs, outputs, review ownership, data exposure, and whether the workflow produces a result that is faster, safer, or more useful than the current process.
AutoGen: implementation checklist
- Define the user, job to be done, and success metric for the tool evaluation workflow.
- Collect real examples, policies, source files, customer questions, or search queries before writing prompts or choosing tools.
- Separate low-risk drafts from decisions that need approval, privacy checks, or senior review.
- Document what the AI is allowed to access, what it must not access, and who signs off before production use.
- Review time to value, adoption rate, cost per workflow, quality review score after a small pilot rather than judging the idea from a demo.
This keeps the work practical. It also gives search engines and AI answer engines a clean factual structure: what the topic is, who it helps, what to do next, and which risks matter before implementation.
Decision criteria for AutoGen
| Decision area | What to check | Production signal |
|---|---|---|
| Intent | Does AutoGen solve a real workflow problem? | The use case has a named owner and measurable outcome. |
| Data | Can the required data be used safely? | Sensitive data is classified and access is controlled. |
| Quality | Can a reviewer judge the output consistently? | Examples, rubrics, or acceptance criteria exist. |
| Scale | Can the workflow be repeated without hero effort? | The process is documented and can be handed to another team member. |
Practical example for AutoGen
A small business could use this article to choose one practical test. For example, a manager might take one customer-facing process, one internal document workflow, or one recurring content task and redesign only that step with AI support. The goal is not to automate the whole business at once; it is to learn where AI Tools creates reliable leverage.
The useful deliverable is a short operating note: the trigger, the source material, the prompt or tool, the review checklist, the escalation rule, and the metric. That note becomes the handover asset for staff training, SEO/GEO content, service delivery, or future agent work.
Risks and controls for AutoGen
The common failure pattern is moving too quickly from a promising idea into an unmanaged workflow. For AutoGen, the risk is not only bad output. It can also be unclear data permission, staff confusion, duplicate content, unreviewed customer advice, or a tool that quietly changes cost or capability.
- Control tool sprawl with a named owner, a review step, and written acceptance criteria.
- Control unclear pricing with a named owner, a review step, and written acceptance criteria.
- Control vendor lock-in with a named owner, a review step, and written acceptance criteria.
- Control unreviewed data sharing with a named owner, a review step, and written acceptance criteria.
Measurement plan for AutoGen
A useful AI or SEO initiative should leave evidence. Track time to value, adoption rate, cost per workflow, quality review score and compare the pilot against the current process. If the measure does not improve, keep the learning but avoid scaling the workflow.
For GEO readiness, the page should also answer the core question directly, define the entities involved, include implementation steps, explain tradeoffs, and link readers to the next relevant AI Kick Start service, guide, tool, or article.
Definitions and entities for AutoGen
For search, GEO, and staff handover, define the core entities in plain language. In this article the important entities are the workflow owner, the AI tool or model, the source material, the review process, the risk boundary, and the measurable business outcome. Clear definitions make the page easier for people to scan and easier for AI answer engines to quote accurately.
- Workflow owner: the person accountable for deciding whether AutoGen belongs in the business process.
- Source material: the documents, examples, policies, URLs, prompts, videos, or customer questions that ground the output.
- Review boundary: the point where a human checks accuracy, privacy, brand voice, or customer impact before the result is used.
- Success metric: the measure that proves whether the tool evaluation workflow is worth repeating.
AutoGen versus doing nothing
Doing nothing is also a decision. The cost may be slow manual work, weaker search visibility, inconsistent advice, duplicated effort, or staff using unmanaged AI tools without a shared process. The practical question is whether a controlled pilot can reduce that cost without creating a larger governance problem.
| Option | When it makes sense | What to watch |
|---|---|---|
| Do nothing | The workflow is rare, low value, or already reliable. | Competitors may improve speed, content depth, or service consistency first. |
| Run a small pilot | The task repeats often and has clear review criteria. | Keep scope tight and measure the result against the current process. |
| Build a production workflow | The pilot is repeatable and risk controls are documented. | Assign ownership, monitoring, training, and a rollback path. |
AI Kick Start handover package for AutoGen
A production handover should be concrete enough that another person can run it. For AutoGen, that means a short brief, a workflow map, approved prompts or tool settings, source material, a review checklist, internal links to supporting resources, and a simple measurement sheet. This is the difference between reading about AI and turning it into operational capability.
That packaging also strengthens E-E-A-T. It shows experience through implementation notes, expertise through decision criteria, authoritativeness through source-aware structure, and trust through risks, controls, and review steps. The article becomes useful even if the reader never buys a tool because it helps them make a better operational decision.
