AutoGen & Multi-Agent Development Services
Your multi-agent prototype demos beautifully. Then it stalls before production, and nobody can agree which framework to commit to while Microsoft moves AutoGen into maintenance mode.
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TRUSTED BY ENTERPRISES
Multi-agent pilots stall when no one owns the framework decision. Microsoft AutoGen is one multi-agent conversation framework, and not always the right one. Kodexo Labs builds production multi-agent systems, then picks the framework each workload actually needs, from 51 products shipped across 25+ industries.
These engagements cover the full path from conversation design to production hardening. Kodexo built the multi-agent system behind Extensiv's plain-English data access, and keeps 94% of the clients it ships for.
Our Core Capabilities:
Conversation design that assigns each agent a clear job.
Agents that run code and query your databases safely.
Human sign-off gates before any agent action goes live.
Monitoring and hardening that keep agents reliable in production.
Honest framework selection, including a Microsoft Agent Framework migration path.
One team accountable from first prototype to live system.
IN THE NEWS
PhD-Level Expert Team
Verified on Clutch.
Years building production AI.
Client retention rate
AutoGen Development Services and Multi-Agent Conversation Frameworks
Most teams do not need one clever agent. They need several agents that pass work between each other, call tools, and pause for a human when the stakes are high. That coordination is multi-agent orchestration, and it is the same pattern behind every production build in our 51-product shipped portfolio.
Multi-Agent Conversation Design
Each agent gets one clear job and a shared conversation to do it in, so work moves without a human relaying messages between steps.
Each agent gets one job, so it reasons, acts, or speaks for the team.
A manager picks who speaks next, so chat reaches an answer, not a loop.
Not sure AutoGen is the right bet?
Our AutoGen experts start with your production requirement, then recommend the framework that fits, even when the answer is not AutoGen at all.
Multi-Agent Systems in Production
Diesel Laptops
Fleet technicians at Diesel Laptops lost more time searching records than fixing trucks. Kodexo built an agentic search system across 160,000 technical records, self-hosted inside the client's own AWS VPC for data control. Technicians now find the right part in seconds, an 85% drop in lookup time, at an Inc. 5000 company.
85%
Faster Lookup
160,000
Records Searched
AWS VPC
Self-Hosted
Extensiv
Extensiv's operations team waited on engineers for every data question they had. We built an agentic system on LangGraph that reads plain-English questions and answers them straight from their own operational database. This Inc. 5000 logistics company now self-serves at 90%+ accuracy across 207 tables and 4 databases, with no engineering ticket required anymore.
90%+
SQL Accuracy
207
Tables
04
Databases
IFPG
Franchise prospects at IFPG were getting broken, error-filled answers across more than 1,000 listings, and leads died at the first click. Kodexo rebuilt the reasoning layer with chain-of-thought prompting, so the system works through each question step by step. HTML errors hit zero, and answer accuracy climbed 85%.
1,000+
Listings
85%
Accuracy Lift
Zero
HTML Errors
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo
Labs
has
met
all
expectations;
the
team
delivers
on
time
and
manages
the
project
seamlessly.
They
respond
promptly
to
needs
and
communicate
effectively
through
virtual
meetings,
Chat,
and
WhatsApp.
Overall,
they're
highly
passionate
about
the
project
and
excel
in
customer
service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- Intake and triage agentsAuditable conversation hand-offsClinician sign-off before recordsDocumentation agents for compliance
Multi-Agent Systems Across 7 Industries
A multi-agent system built for a hospital will not fit a warehouse. The agents, the guardrails, and the human checkpoints shift with the stakes of each vertical. The same agents that automate warehouse workflows on the floor can write and debug their own code for an engineering team. Our AutoGen and multi-agent development work already runs across seven of these industries, and each tab points to where the engagement detail lives.
Your multi-agent demo works. Production is where it stalls.
We build the hardening, monitoring, and human checkpoints that carry a stalled prototype into production-grade AI, before it ever touches a real user.
Compliance-Ready Multi-Agent Deployments
Regulated buyers do not approve an AI system on the strength of a policy PDF. They approve it on controls they can see. Every multi-agent system we ship isolates agent code inside a Docker sandbox, a walled-off container that keeps generated code away from your live systems, and logs every agent action for audit. That is production-grade AI you can put in front of a compliance officer.
Why Enterprises Choose Kodexo Labs for Multi-Agent AI
CTOs who have watched a framework get deprecated ask one question before anything else: will this system still run in two years? Kodexo Labs earns that trust by building production-grade multi-agent systems on the framework each workload actually needs. Our AutoGen development team has driven up to 95% efficiency gains across the deployments we ship. The plain-English data system running today at Extensiv is one of them.
Honest About Microsoft's MAF
Microsoft moved AutoGen into maintenance mode and shipped MAF as its successor. We say so on call one, weigh your workload against AutoGen, LangGraph, CrewAI, and MAF, then map a path that fits.
Framework Chosen by Workload
A team that sells only AutoGen will always find a reason to pick it. Extensiv's ops team now queries 207 tables in plain English at 90%+ accuracy. LangGraph fit that workload, not habit.
Hardened for Real Workloads
Prototypes stall under load. We give each build sandboxed code execution and a self-hosted option. Diesel Laptops runs its parts search in its own AWS VPC, at 160,000 records, lookup time down 85%.
Reasoning You Can Audit
The AI decided is no answer for a board. We build reasoning you can trace and human-in-the-loop checks into each system. IFPG's agent walks through each question, accuracy up 85%, HTML errors zero.
A framework decision shouldn't outlive the vendor that supports it.
Tell us the workload and the constraints. Our AutoGen consulting team names the framework that survives production, not just the demo, before it builds anything.
Recognised By The Platforms That Vet AI Companies
Kodexo Labs is reviewed where technical buyers do their diligence: Clutch and Upwork. Every badge below links to the live profile.
How We Derisk Multi-Agent AI Adoption
Multi-agent projects rarely fail in the demo. They stall in the months after: the framework gets deprecated, an agent makes a call nobody reviewed, or generated code reaches a system it should never touch. We design each risk out before build.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
Our AutoGen Development Process: From Framework Selection to Production
Discovery & Framework Selection
Before a line of code, we map your use case and stress-test framework fit, weighing Microsoft AutoGen against LangGraph, CrewAI, and the Microsoft Agent Framework. You get an honest recommendation, even when the answer is not AutoGen.
Agent & Conversation Architecture
The conversation gets designed first: which agent reasons, which acts, which speaks for you. The ConversableAgent base class, AssistantAgent and UserProxyAgent roles, and a GroupChatManager turn-taking pattern give you a custom multi-agent architecture, not a template.
Build & Tool Integration
Engineering starts against your highest-value workflow. Agents get wired to your tools and data through the Model Context Protocol (MCP) and RAG retrieval, with every code execution boxed inside a Docker sandbox from the first commit.
Test & Validate with Human-in-the-Loop
Human-in-the-loop (HITL) checkpoints go in, then we test for accuracy and hallucination against real traffic. It is the same HITL discipline that took Commercial RE Lease Review's document turnaround from six hours to fifteen minutes, because a person signs off before anything ships. Compliance validation runs alongside, covering HIPAA, GDPR, and SOC 2 obligations where your industry demands them.
Production Deployment & Monitoring
From an AutoGen Studio prototype, we move to a hardened runtime, then instrument it. Observability and request tracing catch accuracy drift before your users do, and monitoring continues long after launch.
Insights From The Kodexo Labs Team
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Are businesses ready for the autonomous AI revolution that’s transforming enterprise operations in 2025? Top agentic AI platforms are enabling companies to deploy intelligent agents that can make decisions, execute tasks, and interact with customers independently, fundamentally changing how organizations operate. This comprehensive guide explores the leading agentic AI platforms, their capabilities, and strategic implementation approaches for modern businesses.
How Multi-Agent Systems Are Solving the Most Complex Problems
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Multi-agent systems enable multiple AI agents to collaborate and solve complex problems that exceed single-agent capabilities, revolutionizing industries from healthcare to smart city management through distributed artificial intelligence.
Agentic AI vs. Generative AI: Key Differences and How to Choose the Right One in 2025
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Explore the key differences between Agentic AI and Generative AI in 2025, focusing on autonomy, decision-making, and content creation. This guide covers their characteristics, use cases, and decision frameworks for businesses aiming to optimize operations or creative workflows.
Frequently Asked Questions
Microsoft AutoGen is an open-source multi-agent conversation framework for building systems where several AI agents pass work between each other, call tools, and run code. It is distinct from AutogenAI, AutoGen Inc., and Autogen.tech, which are unrelated products. Its ConversableAgent base class defines how each agent reasons, acts, and hands off inside one shared conversation.