In 2014, container orchestration was a mess.
Docker Swarm. Mesos. Nomad. CoreOS Fleet. Every major company seemed to be building their own. The concepts were right: you needed something to schedule containers, handle failures, manage networking. But the implementations were fragmented, the APIs incompatible, and nobody was sure what winning even looked like.
Then Kubernetes won. Not overnight. It took until 2017 to become undeniably dominant. It won through a combination of open governance (CNCF), aggressive ecosystem investment, enterprise credibility (Google's production pedigree), and a community that produced tooling faster than any single company could.
We are now in the Docker Swarm era of agent orchestration. The concepts are right. The implementations are fragmented. The dominant standard hasn't emerged yet.
The Current Landscape
Five major frameworks are competing for dominance. I don't think they're equally positioned, and I don't think it's useful to pretend they are.
LangGraph has the most sophisticated state management story: graph-based orchestration with explicit control over agent transitions. It's the one infrastructure engineers gravitate toward because it actually respects the complexity of cyclical workflows. LangChain's enterprise push gives it distribution. But the developer experience is heavier than it needs to be, and the LangChain dependency is a liability for teams that want to own their stack.
CrewAI got adoption fast by mapping to org charts (crews, roles, tasks). It's intuitive for non-infrastructure people, which is both its strength and its ceiling. The team metaphor breaks down when you need fine-grained control over agent communication. I'd bet on it for departmental tools. Not for platform infrastructure.
OpenAI Agents SDK is the one everyone underestimates or overestimates, depending on their priors. Clean API, strong tooling, the full weight of OpenAI behind it. The risk is lock-in. If you build on it, you're betting OpenAI stays the model leader and the orchestration leader. History says that's a bad bet. The best model provider rarely wins the infrastructure layer too.
Then there's the hyperscaler play. Google's ADK and AWS Strands Agents / Bedrock AgentCore are doing what hyperscalers always do: shipping competent, deeply integrated offerings that win by proximity to where enterprise workloads already live. Neither is the best framework. Both will have massive adoption anyway.
The framework that wins this will look like Kubernetes did in 2016: open governance, vendor-neutral enough to earn trust, technically good enough to not get replaced, and backed by an ecosystem that moves faster than any single company. Right now, none of these fully checks those boxes. That's what makes this interesting.
The Standards Layer Matters More Than the Framework
What's more significant than any individual framework is what happened in December 2025.
Anthropic, OpenAI, and Block co-founded the Agentic AI Foundation under the Linux Foundation, with AWS, Google, Microsoft, Bloomberg, and Cloudflare joining as platinum members. Each founder contributed a project: Anthropic's MCP, OpenAI's AGENTS.md, and Block's goose agent framework. Six months earlier, Google had donated A2A to the Linux Foundation as its own project, separate from the new foundation. That puts three complementary standards under Linux Foundation governance:
- MCP (Model Context Protocol): the agent-to-tool integration layer. Millions of monthly SDK downloads, thousands of community servers. Already won.
- A2A (Agent2Agent Protocol): the agent interoperability standard. 100+ supporting companies. Still early, but strategically critical.
- AGENTS.md: open format for giving coding agents project-specific guidance. Tens of thousands of projects adopted.
This is like watching HTTP, TCP/IP, and DNS coalesce in the early Internet. For anyone who lived through the proprietary networking wars of the 1980s or the cloud API fragmentation of the 2010s, this early convergence on shared standards is remarkable. The industry learned something from those costly battles.
The standards layer will outlast any single framework winner. Build on the standards, not on any one framework.
My Predictions
MCP has already won the tool layer. The real question is whether it survives its own success. MCP was designed for capability, not security (I made that case in Trust No Agent). The spec still doesn't enforce authentication, sandboxing, or verification. A major breach involving a malicious MCP server is a when, not an if. If MCP v2 ships authentication and sandboxing primitives before that breach happens, it cements its position permanently. If not, a security-first alternative could fragment the market. The clock is ticking.
A2A is the sleeper that changes everything. Agent interoperability, the ability to compose agents across vendors and frameworks, is the missing architectural primitive. When A2A matures, an Anthropic research agent can delegate to an OpenAI coding agent, which delegates to a Google search agent, all in a single workflow. That's the moment multi-agent AI stops being a feature and becomes a platform. If I were starting a company today, I'd build on A2A. The companies building A2A-native architectures right now will have a structural advantage that's nearly impossible to replicate later.
The framework winner emerges by 2028, and it probably doesn't exist yet. Kubernetes wasn't Docker Swarm improved. It was a fundamentally different architecture that happened to solve the same problem. The agent framework that wins will likely be one that treats open governance, security, and observability as first-class primitives from day one, not one that bolts them on after achieving adoption. Every framework I listed above was designed model-first or developer-experience-first. The winner will be designed trust-first. That's a different starting point, and it usually produces a different product.
Context engineering becomes the new DevOps. The operational discipline of dynamically assembling the right instructions, memory, tool outputs, and agent state for every step of a multi-step workflow is to multi-agent AI what DevOps was to cloud computing. The teams that build this discipline early will be hard to catch, because the skill compounds: better context produces better agent behavior, which produces better training signal, which produces better context strategies.
Prompt engineering is writing a good email. Context engineering is building the mail system.
The Real Question
The orchestration wars have just started. Most of the battles haven't been fought yet. But the shape of the outcome is becoming clear.
Standards win. Open governance wins. Enterprise trust wins. Technical elegance is necessary but not sufficient.
We've been here before. The pattern is unmistakable. And this time, the nodes have judgment.