Nexus vs AutoGen (AG2): Multi-Agent Framework vs Enterprise AI
AutoGen spawned three competing forks: AutoGen 0.4, AG2, and Magentic-One. Nexus deploys production agents in weeks with FDEs. Full comparison with pricing.
Last updated: February 2026
Quick honest summary
AutoGen is an open-source multi-agent conversation framework created by Microsoft Research. It has ~55,000 GitHub stars and is one of the most recognized projects in the AI agent space. AutoGen introduced the idea of multi-agent conversations, where multiple AI agents collaborate through structured dialogue to solve tasks. It has a strong research pedigree and a large community.
There is a significant caveat: AutoGen is in transition. In late 2024, the original creators left Microsoft and forked the project into AG2 (formerly AutoGen), retaining control of the original PyPI packages and Discord community. Microsoft, meanwhile, rebuilt AutoGen from scratch as version 0.4 with a completely different architecture. Then, in October 2025, Microsoft announced that AutoGen and Semantic Kernel are merging into a new unified "Microsoft Agent Framework," with AutoGen entering maintenance mode (bug fixes and security patches only, no new features). The 1.0 GA target for Agent Framework is Q1 2026. For teams evaluating AutoGen today, this means choosing between the AG2 community fork, Microsoft's transitional 0.4 release, or waiting for Agent Framework 1.0.
Nexus is an enterprise AI agent platform paired with white-glove service: Forward Deployed Engineers embedded with your team, change management support, and ongoing optimization. It is not just software you buy and figure out on your own. Nexus is built for enterprises that need agents completing business workflows in production, with business teams owning the outcome, not waiting on engineering.
The right choice depends on your situation. If you have a research-oriented AI team exploring multi-agent architectures and are comfortable navigating framework transitions, AutoGen (or AG2) gives you powerful primitives for experimentation. If the goal is production agents completing enterprise workflows in weeks, with governance, audit trails, and embedded engineering support, that is where Nexus fits. For the complete build-vs-buy decision framework, see our enterprise analysis.
Side-by-side comparison
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When AutoGen is the better choice
AutoGen is a genuinely important project in the AI agent space, and there are scenarios where it is the right call:
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You are researching multi-agent conversation architectures. AutoGen pioneered the concept of agents solving problems through structured conversation. If your team is exploring how multiple agents can collaborate, debate, and refine solutions through dialogue, AutoGen (and the research papers behind it) provides a strong foundation for experimentation and prototyping.
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You have a dedicated AI research team with time to experiment. AutoGen's strength is flexibility in designing conversation patterns between agents. If your team has experienced Python engineers who are comfortable with the framework's learning curve, and they have bandwidth specifically for AI research (not competing with production deadlines), AutoGen gives them powerful building blocks.
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You want to explore Magentic-One's generalist multi-agent team. Magentic-One is a pre-built team of five specialized agents (Orchestrator, WebSurfer, FileSurfer, Coder, and ComputerTerminal) that can handle open-ended tasks. For teams interested in seeing what multi-agent collaboration looks like in practice without building from scratch, it is a useful starting point.
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Your use case is experimental or academic. Novel reasoning architectures, multi-agent debate patterns, research simulations, or agent designs that push the boundaries of what is possible. AutoGen's open-ended design supports this kind of exploration.
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You are already invested in the Microsoft ecosystem and plan to migrate to Agent Framework. If your team is building on Azure and plans to adopt Microsoft Agent Framework when it reaches 1.0 GA, starting with AutoGen 0.4 concepts gives you a head start on the migration. Just be aware that the transition will require work.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they evaluated developer frameworks (or tried building internally), realized the engineering investment was too high for internal business workflows, and chose a platform + service approach instead.
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You need agents in production, not in a research notebook. AutoGen excels at prototyping multi-agent conversations. Getting those conversations into production, with reliability, monitoring, security, audit trails, and integration with enterprise systems, is a separate (and much larger) problem that AutoGen does not solve. Nexus is built specifically for production deployment. Most agents go live within 2-6 weeks.
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Your engineering team is already stretched, and this is not their core product. Building production-grade multi-agent systems with AutoGen requires designing conversation patterns, building infrastructure, implementing security, creating monitoring, and maintaining everything as the framework evolves (or migrates to Agent Framework). Lambda, a $4B+ AI infrastructure company with world-class engineers, ran this calculation and concluded: the opportunity cost is too high. Nexus removes the engineering dependency entirely.
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Business teams need to own the agents, not file tickets with engineering. With AutoGen, every modification requires engineering time: updated conversation flows, new tool integrations, changed agent behaviors. With Nexus, the business teams who understand the workflows own and iterate on the agents directly. When Lambda's Head of Sales Intelligence needed to adjust data sources, he did it himself. No engineering tickets.
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You cannot afford framework instability in production. AutoGen's transition from 0.2 to 0.4 was a complete architectural rewrite, breaking backward compatibility. The AG2 fork created package naming confusion. The upcoming migration to Microsoft Agent Framework adds another transition. For production enterprise workflows, framework instability creates real risk. Nexus provides a stable, continuously updated platform with no migration burden on your team.
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You want enterprise governance without building it yourself. AutoGen has no built-in audit trails, no compliance certifications, no role-based access control, no decision traceability. For regulated industries and public companies, building all of this from scratch on top of an open-source framework is a major engineering project. Nexus ships with SOC 2 Type II, ISO 27001, ISO 42001, GDPR compliance, full audit trails, and decision traceability from day one.
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Your workflows span multiple enterprise systems. Connecting AutoGen agents to CRMs, ERPs, communication tools, and custom APIs requires building and maintaining each integration individually. Unlike rule-based workflow tools that connect apps but cannot reason through exceptions, Nexus connects to 4,000+ enterprise systems natively and deploys across any channel: Slack, Teams, WhatsApp, email, phone, web.
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You need more than software. You need a partner. Most vendors sell software and disappear. AutoGen is open-source with no enterprise support. Nexus embeds Forward Deployed Engineers with your team. They help identify the highest-impact use cases, design agents that fit your specific reality, handle integration complexity, manage change, and optimize continuously. Deploying AI at scale is 10% technology and 90% organizational change.
What enterprises experienced
Lambda: a $4B+ AI company chose to buy instead of build
This is the proof point that matters most for anyone evaluating developer frameworks versus a platform approach.
Lambda, a $4B+ AI company with world-class engineers, evaluated building multi-agent conversation systems with AutoGen before choosing Nexus's platform approach. They build supercomputers for AI training and inference. Their customers include some of the world's top AI labs. If any company had the engineering depth and research pedigree to build custom multi-agent systems, it was Lambda.
They chose to buy because the build time, framework instability, and permanent engineering investment could not be justified against core product work.
What they tried first: Lambda explored open-ended AI agents (like ChatGPT Deep Search) and traditional workflow automation. Open-ended agents were intelligent but inconsistent: same question, different results every time. Workflow automation was reliable but rigid: heavy hard-coding, brittle integrations, breaks when systems change. Neither worked for enterprise-grade sales intelligence.
What they built with Nexus: Joaquin Paz, Lambda's Head of Sales Intelligence, built an autonomous research agent that monitors 12,000+ enterprise accounts annually, identifies buying signals across dozens of data sources, and synthesizes competitive intelligence. The critical detail: Joaquin is not an engineer. He built this in days.
The results:
- $4B+ in cumulative pipeline identified across accounts Lambda was not actively monitoring
- 24,000+ research hours added annually (equivalent to 12 full-time analysts)
- 12,000+ enterprise accounts analyzed with deep intelligence
- Deployed in weeks, not the months it would have taken to build internally
"I'm not an engineer. I built this in days. With the automation tools we looked at before, I would have needed to spec everything out and wait months for development."
"We looked at open-ended AI agents; they were smart but inconsistent. Same question, different answer every time. We looked at traditional automation; it was reliable but felt heavy, lots of hard coding. With Nexus, we got both: intelligent and consistent."
-- Joaquin Paz, Head of Sales Intelligence, Lambda
Why Lambda's CTO chose to buy: The opportunity cost of engineering time was too high. Every hour Lambda's engineers spent building internal automation was an hour not spent on their core product: AI cloud infrastructure for customers. They deployed in days what would have taken months internally.
Lambda has since expanded from a single agent to a fleet across sales and marketing. They are building what they call an "agentic layer," a persistent intelligent system across their go-to-market organization. Anticipated value: more than $7M by 2026.
Orange Group: 120,000+ employees, business team deployed in 4 weeks
Orange Group is a multi-billion euro telecom operator with 120,000+ employees across Europe and Africa. They have significant internal engineering resources. They had every option available: build internally, hire an agency, deploy Copilot.
Their business team (not engineering) built customer onboarding agents using the Nexus platform. Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. 100% adoption. Business teams own the agents. No engineering dependency.
European telecom: tried Copilot Studio for 6 months, zero production use cases
A multi-billion euro European telecom operator spent 6 months trying to deploy AI through Microsoft Copilot Studio. The result: zero production use cases. The gap between demo and deployment was too large. After switching to Nexus, they deployed a dozen agents across their operations. The difference was not just the platform. It was the Forward Deployed Engineers who understood how to move from pilot to production in a regulated enterprise environment.
Key differences explained
Research framework vs. enterprise platform + service: different problems, different models
This is the core distinction.
AutoGen is a research framework that became popular because it introduced an elegant idea: agents that solve problems by talking to each other. Multi-agent conversations, where a "user proxy" agent collaborates with an "assistant" agent (and potentially others), provided a new paradigm for AI systems. The research is real, the ideas are influential, and the community is large.
But research frameworks and enterprise production platforms solve different problems. AutoGen gives you the building blocks for multi-agent conversations. It does not give you production infrastructure, enterprise integrations, security, compliance, audit trails, monitoring, or a path to deployment. Those are separate, significant engineering projects.
Nexus is a platform + service. Business teams build and deploy agents that complete their workflows. The platform handles infrastructure, integrations, security, and compliance. Forward Deployed Engineers work alongside your team to identify use cases, design agents, handle complexity, and optimize over time. The business team focuses on outcomes, not architecture.
The AutoGen fragmentation problem: three paths, unclear future
Teams evaluating AutoGen today face an unusual situation. There are effectively three options:
AG2 (the community fork): Maintained by AutoGen's original creators who left Microsoft. Retains the familiar 0.2 architecture. Controls the original PyPI packages (pyautogen, autogen, ag2). Has its own community and governance. Good for teams who want stability and backward compatibility with the original design.
AutoGen 0.4 (Microsoft's rewrite): A complete architectural overhaul based on an asynchronous, event-driven actor model. Different API, different paradigms, different packages. Not backward compatible with 0.2 code. Now in maintenance mode as Microsoft shifts focus to Agent Framework.
Microsoft Agent Framework (the future): The merger of AutoGen and Semantic Kernel into a unified framework. Public preview launched October 2025. 1.0 GA targeted for end of Q1 2026. This is where Microsoft is putting its investment going forward.
This fragmentation creates real risk for enterprise teams. Which version do you build on? How much will you need to rewrite when Agent Framework reaches 1.0? What happens to AG2 if the community fragments further? For teams building internal business workflows, this uncertainty is a meaningful consideration.
Nexus does not have this problem. It is a single, stable platform with continuous updates, backward compatibility, and a dedicated team ensuring your agents keep working as the platform evolves.
The production gap: prototype to deployment is the hardest part
AutoGen makes it relatively easy to prototype a multi-agent conversation. Define a few agents, set up their conversation patterns, and watch them collaborate. The demos are impressive. The research papers are compelling.
The gap between a working prototype and a production enterprise deployment is where most projects stall. Production requires:
- Infrastructure: Where do the agents run? How do you handle scaling, failover, and monitoring?
- Security: How do you control access? How do you prevent data leakage between conversations?
- Integrations: How do agents connect to your CRM, ERP, communication tools, and databases?
- Governance: How do you audit what agents did, why they made specific decisions, and what data they accessed?
- Reliability: How do you handle agent conversations that go off track, loop indefinitely, or produce inconsistent results?
- Maintenance: How do you update agents when business logic changes, and who does it?
AutoGen provides none of this. These are all engineering projects your team would need to build and maintain. According to Gartner, over 40% of agentic AI projects will be canceled by 2027 due to escalating costs, unclear business value, or inadequate risk controls. The production gap is a primary reason.
Nexus is built specifically to close this gap. Production infrastructure, 4,000+ integrations, enterprise governance, and Forward Deployed Engineers who help you get from pilot to production in weeks.
Forward Deployed Engineers: why Nexus is a solution, not just software
Every Nexus engagement includes Forward Deployed Engineers (FDEs), real engineers embedded with your team who:
- Identify the highest-impact use cases first. Not guessing based on templates, but analyzing your specific operations to find where agents deliver the most value.
- Design agents that fit your reality. Not generic configurations, but agents tailored to your workflows, systems, edge cases, and business logic.
- Handle integration complexity. So your team does not have to learn a new platform or pull engineers off product work.
- Manage organizational change. Because deploying AI at scale is 10% technology and 90% organizational change. FDEs help frame the change, train teams, build confidence through small wins, and address concerns.
- Optimize continuously. Agents improve with use. FDEs help analyze performance, refine escalation logic, and scale agents to new teams and processes.
This is why Nexus converts 100% of POCs to annual contracts. The engagement is structured to deliver measurable value before you commit. Open-source frameworks cannot provide this. It is not just about the code; it is about the partnership.
The opportunity cost calculation: Lambda proved the math
The decision between a developer framework and a platform approach often reduces to a single question: what is the opportunity cost of your engineering team's time?
Building production multi-agent systems with AutoGen is not just writing agent conversation logic. It is designing the architecture, building integrations, implementing security, setting up monitoring, handling conversation failures, managing infrastructure, navigating framework transitions, and maintaining everything as systems change. For a single agent system, that is weeks to months. For an agent fleet, it is a permanent engineering investment.
Lambda, with $4B+ in valuation, $500M+ ARR, and engineers who build AI infrastructure for a living, ran this calculation and concluded: the opportunity cost is too high. Every engineering hour spent building internal agents was an hour not spent on the core product.
Frequently asked questions
Can our engineering team use AutoGen alongside Nexus?
Yes. Some enterprises use developer frameworks for product-facing AI capabilities (where deep customization and architectural ownership matter) and Nexus for internal business workflows (where speed, business ownership, and embedded support matter). The two solve different problems for different teams. Lambda made exactly this distinction: their engineers focus on their core AI infrastructure product, while business teams own their operational agents through Nexus.
AutoGen has ~55,000 GitHub stars. Does popularity matter?
Stars reflect community interest, especially from the research and developer community. AutoGen deserves credit for popularizing multi-agent conversations as a paradigm. But GitHub stars do not indicate production readiness, enterprise adoption, or deployment success. Many starred projects are used for experimentation and learning, not production workloads. The question for enterprise teams is not how popular a framework is, but whether it can deliver production agents with governance, reliability, and support.
What about the AG2 fork? Should we use that instead of Microsoft's AutoGen?
This depends on your priorities. AG2 maintains the original AutoGen 0.2 architecture, which is more stable and familiar. Microsoft's AutoGen 0.4 is a complete rewrite with a different API. If you prefer continuity, AG2 is the more conservative choice. If you want to align with Microsoft's long-term direction, AutoGen 0.4 (and eventually Agent Framework) is the path. Either way, the production deployment challenges remain the same: you still need to build infrastructure, security, integrations, and governance yourself. Nexus handles all of that as part of the platform.
Should we wait for Microsoft Agent Framework 1.0?
Microsoft Agent Framework 1.0 GA is targeted for end of Q1 2026. It merges AutoGen's multi-agent concepts with Semantic Kernel's enterprise features. It is worth watching. But even when it ships, Agent Framework is still a developer framework. Your engineering team will still need to build, deploy, maintain, and iterate on the agents. The production gap, governance requirements, and integration complexity do not disappear because the framework improves. If your goal is production agents completing business workflows in the near term, waiting 6+ months for a framework release (and then spending months building on it) may not be the right trade-off.
We have strong AI engineers. Why would we choose Nexus over building with AutoGen?
Having strong engineers is exactly the reason to consider whether their time is best spent on internal business workflows. Lambda has world-class AI engineers who build supercomputers for a living, and chose to buy instead of build. The question is not capability. It is opportunity cost. Your engineers could build this. But should they, when their time could be spent on your core product?
How does Nexus compare to AutoGen on multi-agent capabilities?
AutoGen's multi-agent conversation patterns are more flexible at the research level. You can design arbitrary conversation topologies between agents. Nexus approaches multi-agent coordination differently: agents are designed around enterprise workflows, with built-in escalation, routing, human-in-the-loop, and handoff patterns. For business workflows (where the patterns are well-understood and reliability matters more than novelty), Nexus's approach delivers faster. For open-ended research on new multi-agent architectures, AutoGen gives you more freedom.
What does the 3-month POC look like?
Every engagement starts with a 3-month proof of concept tied to specific, measurable outcomes defined upfront. Most agents are in production within the first 2-6 weeks. A Forward Deployed Engineer is embedded with your team for the entire period. You see the results, measure the impact, and decide whether to continue. You can exit anytime. This is why our POC-to-contract conversion rate is 100%: we do not move forward unless the value is clear.
Is AutoGen really free?
The open-source framework is free. But production deployment is not. All infrastructure costs (compute, storage, networking), security implementation, monitoring and observability, enterprise integrations, and ongoing maintenance are your responsibility. Beyond direct costs, the real expense is engineering time: building, deploying, maintaining, and iterating on multi-agent systems, while navigating framework transitions. That is the cost Lambda concluded was too high.
Worth exploring?
If your team has been evaluating multi-agent frameworks and wrestling with the gap between prototype and production (how to get agents into enterprise workflows reliably, who maintains them, how to handle governance and compliance, how to navigate AutoGen's framework transition), it might be worth seeing how Lambda approached the same decision.
Lambda is a $4B+ AI company with world-class engineers who build AI infrastructure for a living. They explored open-ended AI agents and traditional automation. Neither worked. They concluded the opportunity cost of building was too high. They deployed in days what would have taken months internally. Their non-technical team owns the agents. Anticipated value: more than $7M by 2026.
Every Nexus engagement starts with a 3-month proof of concept tied to specific outcomes. Forward Deployed Engineers work alongside your team from day one. You see results before committing. You can exit anytime.
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Every engagement starts with a 3-month proof of concept tied to specific, measurable business outcomes. Forward Deployed Engineers embed with your team from day one.