DIY / raw API · vs. In-house, on raw model provider APIs

Build your own agent orchestration

Building a multi-agent system directly on raw model APIs takes a weekend to prototype but months to make production-ready — the observability, routing validation, and bounded-chain infrastructure a team usually doesn't build until after the first incident.

Short answer: building a multi-agent prototype directly on a model provider's API is genuinely fast — a working demo in a weekend is realistic. The gap is everything between "it works in the demo" and "it's safe to leave running unattended in production," which is where most in-house builds stall.

The part that's actually easy

Calling a model, handling a tool call, chaining a couple of steps together — every major model provider's API makes this straightforward, and a team with reasonable engineering skill can have something that looks like a working multi-agent system running in days, not months. This isn't a strawman: it's the normal, correct first step, and building it in-house is a completely reasonable way to validate whether multi-agent is the right shape for a problem at all.

The part that isn't

The infrastructure gap shows up once that prototype needs to run for real: session management that survives a process restart, SSE streaming instead of blocking request/response, routing logic that's checked against something rather than trusted to a prompt, a durable record of what happened in a given run for when something goes wrong, and a hard limit on how deep and how expensive a chain of agent-to-agent calls can get before it's stopped.

None of these are individually hard engineering problems. Collectively, they're the reason "the prototype works" and "this is production-ready" are different milestones, often separated by months of work that isn't specific to the product being built — it's the same plumbing any team building multi-agent systems eventually needs.

What a managed platform actually removes from that list

Anter's orchestrator ships validated routing (write-time allowlist plus runtime existence check on every handoff), durable atomic persistence of every routing event, SSE streaming with resumable replay, fail-closed unattended approvals, and bounded agent-to-agent chains — as the default runtime behavior, not infrastructure a team builds and maintains themselves. The trade-off is real: it's a managed platform, not source code you own outright. For a team whose actual product isn't agent infrastructure, that trade-off is usually the right one.

See the full side-by-side comparison for how this stacks up against Claude Managed Agents, OpenAI AgentKit, CrewAI, and Base44 specifically.

Frequently asked

Is it hard to build a multi-agent prototype on raw model APIs?

No — a working prototype, one agent calling a model and handling a tool call or two, is genuinely a weekend of work with any current model provider's API. The gap isn't building something that works once; it's the infrastructure a system needs before it's safe to leave running unattended in production.

What's usually missing from a hand-built agent system in production?

In practice, the same handful of things: session state that survives a restart, streaming responses instead of blocking calls, routing logic that's validated rather than a prompt you hope holds together, an audit trail for reconstructing why an agent did what it did, and a way to bound runaway multi-step chains before they run away in cost or latency. None of it is hard individually — together it's the difference between a demo and a production system.

In the Anter docs

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Last updated August 4, 2026