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Three years into the agent era, the build-versus-buy debate has finally split into a third option: orchestrate. The question is no longer whether to use LangGraph or Anthropic Claude. The question is which workflows belong in a DIY framework, which belong on a vendor agent runtime, and which belong on an orchestration layer that sits above both. Get this wrong and you spend 2026 ripping out an architecture you committed to in Q1.
This is the decision framework we use with engineering leadership teams sizing their first or second agent platform investment. It assumes you have moved past prototype, you have at least one agent in production or near it, and the next call is structural.
The Three Postures
Every agent program in 2026 is operating in one of three postures, whether the team articulates it or not. Naming the posture is the first work.
- Build. You own the orchestration code. LangGraph, CrewAI, AutoGen, Pydantic AI, Mastra, or a homegrown state machine. Maximum control, maximum maintenance, maximum hiring bar.
- Buy. You consume an agent runtime as a product. OpenAI Assistants, Anthropic Claude with native tools and MCP, Microsoft AutoGen Studio, Google Vertex AI Agent Builder, AWS Bedrock Agents. Fast time to value, opinionated runtime, vendor lock-in proportional to the surface area you adopt.
- Orchestrate. You sit a control plane above multiple model and runtime providers. Vellum, Galileo, LangSmith Hub, Humanloop, Arize Phoenix, Braintrust. You write less infrastructure, you keep optionality, you pay a per-seat or per-trace tax.
When to Build
Build when the agent encodes proprietary workflow that is itself a competitive moat. This is the test that matters and the one most teams flunk. If your agent automates a workflow that any competitor could buy off the shelf, you are not building a moat by writing the orchestration code yourself. You are building maintenance. The agents that justify a custom build are the ones where the state graph itself reflects domain expertise that took the company years to accumulate.
Concrete signals that build is the right call: the workflow has more than 15 distinct states, branches on domain-specific business rules at most transitions, integrates with at least three internal systems that no vendor knows about, and is owned by a team that already runs production stateful systems competently. LangGraph and CrewAI are the mainstream choices for Python shops. Mastra is gaining ground in TypeScript. Pydantic AI is the sleeper pick for teams that already live in the Pydantic ecosystem and want strong typing without the FAANG-scale baggage of LangGraph.
The Hidden Build Cost
Build means you own checkpointing, retry semantics, tool versioning, observability, eval, prompt management, and the on-call rotation when an agent loops at 3 a.m. and consumes $4,000 of inference before your circuit breaker fires. Budget two senior engineers for the first agent and 0.5 to 1 FTE per additional agent in steady state. If you cannot fund that, do not build.