AI Frameworks & Agent Orchestration - Microsoft AutoGen
Multi-agent AI that actually runs
in your operations, not just a notebook
We design, build, and operationalize Microsoft AutoGen agent pipelines for mid-market companies - connecting AssistantAgent, UserProxyAgent, and GroupChat workflows to the real systems your revenue and ops teams depend on.
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Operators and teams we've worked with
AutoGen pilots stall because nobody owns the production path
Microsoft AutoGen is genuinely powerful for orchestrating multi-agent conversations - AssistantAgent and UserProxyAgent pairs can decompose complex tasks, call tools, write and execute code, and loop until a goal is met. The problem most mid-market teams hit is that the framework is built for researchers and engineers first. Getting a GroupChat workflow to hand off reliably between a data-retrieval agent, a reasoning agent, and a human-approval step in a notebook is one thing. Getting that same workflow to run on a schedule, authenticate against your CRM, handle failures gracefully, log every turn for compliance, and not hallucinate through a customer-facing process is a different problem entirely - and most internal teams lack the prior production-agent experience to bridge it.
Revenue Institute takes AutoGen from proof-of-concept to a system your operations team can own. We define the agent graph, set termination conditions that prevent runaway loops, wire in function-calling tools against your real data sources, add human-in-the-loop checkpoints where the business requires them, and deploy into an environment with observability and error handling baked in. The result runs in production, not in a shared notebook nobody touches after the demo.
What we do with Microsoft AutoGen
What we build inside your AutoGen deployment
Agent graph design and role definition
We map your business process to AutoGen's agent primitives - AssistantAgent, UserProxyAgent, and GroupChat - defining each agent's system prompt, tool access, and termination logic before a line of code. Poorly scoped roles are the most common reason workflows loop endlessly in production.
Function-calling tool integration
AutoGen's function-calling layer lets agents invoke real tools - CRM lookups, database queries, API calls, file reads - rather than hallucinating data. We build and register those functions, handle authentication, enforce input/output schemas, and scope each agent's tool access to its role.
Human-in-the-loop checkpoint design
AutoGen's UserProxyAgent supports configurable human input modes - ALWAYS, TERMINATE, or NEVER. We design the approval gates your compliance or operations team needs, so agents handle routine steps autonomously while escalating decisions that require human sign-off, without breaking the conversation flow.
Termination and loop-control logic
Runaway agent loops are a real production failure mode in AutoGen. We set explicit is_termination_msg functions, max turn limits, and fallback conditions so every workflow has a defined exit path - whether the task completes, hits a data error, or requires human intervention.
Observability and conversation logging
Every agent turn in AutoGen produces a structured message object. We capture and store those conversation histories, route them to your logging stack, and surface the metrics that matter - task completion rate, average turns per workflow, and tool call failure rate.
Deployment and scheduling infrastructure
Running AutoGen in production means moving beyond local Python. We containerize your agent workflows, wire them into your orchestration layer - Azure Container Apps, a job scheduler, or an event-driven trigger - and add the retry and alerting logic your ops team needs.
Our framework
How a Microsoft AutoGen engagement runs
Discovery and process mapping
We spend the first phase understanding the process you want to automate - the data sources, the decision points that require judgment, the unacceptable failure modes, the systems agents must touch. We leave with a written agent graph spec, not a vague roadmap.
Build and integration
We build the AutoGen pipeline against your actual systems - writing tool functions, configuring agent roles and prompts, setting termination logic, integrating your CRM, database, or API layer. We run it against real data in staging until quality and reliability meet your bar.
Handoff and operationalization
We deploy into your production environment, document the agent graph and every configuration decision, and train the internal team that will own the system. We stay available for a defined support window after go-live to catch edge cases staging did not surface.
What AutoGen actually does and where it earns its place in a mid-market stack
Microsoft AutoGen is a multi-agent conversation framework. Its core idea is that complex tasks are better handled by a coordinated group of specialized agents than a single monolithic prompt. An AssistantAgent receives a goal, reasons about it, and produces a response or a tool call. A UserProxyAgent executes that call, inspects the result, and accepts it or sends it back for revision. A GroupChat manager routes messages between AssistantAgents with different expertise - one on data retrieval, another on analysis, another on formatting output. The framework handles the message-passing so you focus on what each agent does and when the conversation should stop. That maps well onto processes that have always required multiple roles: research and synthesis, multi-step data enrichment, draft-review-approve document workflows, and internal triage where the next action depends on what earlier steps found.
Where AutoGen earns its place is in tasks where the agent needs to self-correct. Because agents can inspect the output of a tool call and decide whether it answered the question, they can retry with different parameters, ask a clarifying sub-question, or escalate to a human rather than silently returning a wrong answer. That feedback loop is what makes multi-agent orchestration worth the added complexity over a chain of static prompts. The trade-off is that it requires careful design - termination conditions, tool schemas, agent role boundaries - and that design work is where most mid-market teams get stuck.
The production gap that most AutoGen implementations fall into
The gap between a working AutoGen notebook and a trustworthy production system is wider than most teams expect. In a notebook, you watch every agent turn in real time, restart the kernel when something goes wrong, and inspect intermediate outputs. In production, the workflow runs unattended, failures must surface as alerts rather than tracebacks, every conversation needs logging for audit, and the system must handle edge cases that never appeared in your ten development runs. AutoGen does not provide that operational layer out of the box - it provides the orchestration primitives, and the production infrastructure is yours to build around it. That is not a criticism; it is what the framework is designed to be.
What Revenue Institute brings is the experience of building that production layer repeatedly across business contexts. We know which AutoGen configuration decisions cause problems at scale - overly broad system prompts that produce inconsistent tool calls, missing termination conditions that push workflows past token limits, tool functions that ignore API rate limits and fail the entire conversation mid-run. We also know how to scope the initial workflow narrowly enough to go live and prove value quickly, rather than automating an entire department in the first engagement. The teams that get the most out of AutoGen pick one well-defined process, instrument it properly, and use what they learn in production to inform the next.
We're vendor-agnostic
Other AI Frameworks & Agent Orchestration platforms we specialize in
Not sure Microsoft AutoGen is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
Microsoft AutoGen questions, answered
How is AutoGen different from just calling an LLM with a prompt?
A single LLM call is a one-shot input-output exchange. AutoGen orchestrates a conversation between multiple agents that can each have different roles, tools, and instructions. One agent might retrieve data, another reasons over it, a third writes and executes code to validate the answer, and a UserProxyAgent decides whether to accept the result or ask for a revision. That loop structure is what makes AutoGen useful for multi-step business processes that a single prompt cannot handle reliably.
Do we need Azure or Microsoft infrastructure to run AutoGen?
No. AutoGen is an open-source Python framework and is not locked to Azure. It works with any OpenAI-compatible API endpoint, including OpenAI directly, Azure OpenAI Service, or locally hosted models. That said, if your organization already runs on Azure, the integration with Azure OpenAI Service and Azure Container Apps is straightforward and often the path we recommend for mid-market deployments that need enterprise access controls and compliance logging.
What kinds of business processes are actually a good fit for AutoGen?
AutoGen works well for processes that involve multiple sequential steps, require tool calls to fetch or write data, and benefit from an agent being able to self-correct when an intermediate result is wrong. Good fits include automated research and summarization workflows, multi-step data enrichment pipelines, internal triage and routing logic, and draft-review-revise document workflows. It is not the right tool for simple single-turn question-answering or for processes where latency under one second is a hard requirement.
How do you prevent agents from looping forever or producing bad outputs?
AutoGen gives you several control mechanisms - max consecutive auto replies, custom is_termination_msg functions that check for a specific string or condition in the agent's output, and the ability to route back to a human via the UserProxyAgent before the workflow continues. We design those controls as part of every engagement. The failure mode we see most often is teams skipping termination logic in the prototype phase and then being surprised when the same gap causes problems in production.
Can AutoGen agents connect to our existing CRM or ERP?
Yes, through AutoGen's function-calling capability. We write Python tool functions that wrap your CRM or ERP API, register them with the appropriate agent, and define the input/output schema so the LLM knows how to invoke them correctly. The agent can then look up records, write updates, or trigger workflows in your existing systems as part of the automated conversation. Authentication and rate-limit handling are part of what we build.
How long does it take to go from nothing to a production AutoGen workflow?
It depends on the complexity of the process and the state of your underlying data and API infrastructure. A focused single-workflow engagement - one well-scoped business process with two to three agents and a defined set of tool integrations - typically moves from kickoff to production deployment inside the first 100 days. The variable that most often extends timelines is discovering mid-build that the underlying data source is inconsistent or that the API we need to call does not exist yet.
Do we need a data science team internally to maintain AutoGen after you build it?
Not necessarily. AutoGen workflows are Python code, and a developer comfortable with Python and REST APIs can maintain and extend what we build. We document every configuration decision - agent roles, system prompts, tool schemas, termination logic - so the system is not a black box after we hand it off. For teams without internal Python capability, we can scope a managed support arrangement, but our goal is always to leave you operationally independent.
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