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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$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Trusted by the teams we build 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 reliably hand off between a data-retrieval agent, a reasoning agent, and a human-approval step inside a Jupyter notebook is one thing. Getting that same workflow to run on a schedule, authenticate against your CRM, handle failures gracefully, log every agent turn for compliance, and not hallucinate its way through a customer-facing process is a completely different problem - and most internal teams do not have the time or the prior production-agent experience to bridge that gap.
Revenue Institute takes AutoGen from proof-of-concept to a system your operations team can actually 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 is a multi-agent workflow that runs in production, not one that lives 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 is written. Poorly scoped agent roles are the single most common reason AutoGen workflows loop endlessly or produce unreliable outputs 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 tool functions, handle authentication, enforce input/output schemas, and make sure each agent only has access to the tools its role requires.
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 actually needs, so agents handle routine steps autonomously while escalating decisions that require a 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 successfully, hits a data error, or requires human intervention.
Observability and conversation logging
Every agent turn in AutoGen produces a structured message object. We instrument your deployment to 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, tool call failure rate - so you can see what the agents are actually doing.
Deployment and scheduling infrastructure
Running AutoGen in production means moving beyond local Python execution. We containerize your agent workflows, wire them into your orchestration layer - whether that is Azure Container Apps, a job scheduler, or an event-driven trigger from your existing stack - and set up the retry and alerting logic your ops team needs to trust the system.
Our framework
How a Microsoft AutoGen engagement runs
Discovery and process mapping
We spend the first phase understanding the specific business process you want to automate - the data sources involved, the decision points that require judgment, the failure modes that would be unacceptable in production, and the systems the agents need to touch. We leave this phase with a written agent graph spec, not a vague AI roadmap.
Build and integration
We build the AutoGen agent pipeline against your actual systems - writing tool functions, configuring agent roles and system prompts, setting termination logic, and integrating with your CRM, database, or API layer. We run the workflow against real data in a staging environment and iterate until the output quality and reliability meet the bar you set in discovery.
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 going forward. We stay available for a defined support window after go-live to catch edge cases the staging environment 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 by a single monolithic prompt. An AssistantAgent receives a goal, reasons about it, and produces a response or a tool call. A UserProxyAgent can execute that tool call, inspect the result, and either accept it or send it back for revision. A GroupChat manager can route messages between multiple AssistantAgents with different expertise - one focused on data retrieval, another on analysis, another on formatting output for a downstream system. The framework handles the message-passing infrastructure so you can focus on defining what each agent is supposed to do and when the conversation should stop. That architecture maps well onto real business processes that have always required multiple people with different roles to complete: research and synthesis tasks, multi-step data enrichment, draft-review-approve document workflows, and internal triage pipelines where the right next action depends on what earlier steps found.
Where AutoGen earns its place over simpler approaches 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 the capability that makes multi-agent orchestration worth the added complexity compared to a chain of static prompts. The trade-off is that the framework requires more careful design - termination conditions, tool schemas, agent role boundaries - than a single-prompt approach, 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 can watch every agent turn in real time, restart the kernel when something goes wrong, and manually inspect intermediate outputs. In production, the workflow runs unattended, failures need to surface as alerts rather than tracebacks, every agent conversation needs to be logged for audit purposes, and the system needs to handle the edge cases that did not appear in the ten test runs you did during development. AutoGen does not provide that operational layer out of the box - it provides the agent orchestration primitives, and the production infrastructure is your responsibility to build around it. That is not a criticism of the framework; it is simply what it is designed to be.
What Revenue Institute brings is the experience of having built that production layer repeatedly across different business contexts. We know which AutoGen configuration decisions cause problems at scale - overly broad agent system prompts that produce inconsistent tool calls, missing termination conditions that cause workflows to exceed token limits, tool functions that do not handle API rate limits and cause the entire agent conversation to fail mid-run. We also know how to scope the initial workflow narrowly enough that it can go live and prove value quickly, rather than trying to automate an entire department in the first engagement. The teams that get the most out of AutoGen are the ones that pick one well-defined process, instrument it properly, and use what they learn from running it in production to inform the next workflow they build.
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 in a matter of weeks, not months. 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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