The AI agents market is growing at a pace that should make every developer pay attention. According to MarketsandMarkets, it's projected to grow from $7.84 billion in 2025 to $52.62 billion by 2030 — a 46.3% compound annual growth rate. And by 2026, 35% of organizations are expected to have broad AI agent adoption in place.
But here's the part that most coverage misses: the hard part isn't building an agent. It's building one that you can actually trust, audit, and hand off to someone else.
That's where managed AI agents come in — and specifically, the manifest.
The core idea: A managed AI agent isn't just a prompt with a loop. It's an agent defined by a structured specification — a manifest — that explicitly describes what the agent can do, what tools it can call, and how it should behave. Think of it as infrastructure-as-code, but for agent behavior.
If you've ever shipped a brittle agent built on layered system prompts and hoped it wouldn't go sideways in production, this will feel like a breath of fresh air.
What Is a Managed AI Agent?§
A managed AI agent is an agent whose behavior, capabilities, and constraints are defined and governed through a formal specification rather than embedded implicitly in code or prompts.
The word "managed" is doing a lot of work here. It signals that the agent operates within a defined boundary — one that can be reviewed, updated, versioned, and enforced. This is fundamentally different from a chatbot with a long system prompt or a one-off script that calls an LLM.
The three things a managed agent always has§
- A defined identity: What is this agent? What is its purpose? What model or runtime does it use?
- Explicit capabilities: What tools, APIs, or actions can it invoke? What data can it access?
- Behavioral constraints: What is it not allowed to do? How should it handle edge cases, errors, or out-of-scope requests?
In a well-designed managed agent setup, all three of these live in the manifest — not scattered across environment variables, prompt files, and README notes.
This matters more than it might sound. When something goes wrong with an unmanaged agent (and it will), you're debugging a black box. With a managed agent, you have a contract to check against.
The Manifest: A Contract for Agent Behavior§
The manifest is the central artifact in a managed agent architecture. It's a structured text file — typically YAML or JSON — that serves as the single source of truth for what an agent is and what it's allowed to do.
"The manifest becomes the contract for what the agent is allowed to do, which tools and actions it can call, and how it's evaluated." — Workativ AI Agent Blog
That framing — contract — is the right one. In software, we're used to contracts: API schemas, type definitions, interface declarations. The agent manifest brings the same discipline to AI behavior.
What a manifest typically contains§
| Field | What it defines |
|---|---|
name / description | Agent identity and purpose |
model | Which LLM or runtime powers the agent |
tools | Explicit list of callable tools or APIs |
instructions | Behavioral rules and task framing |
data_access | What knowledge sources or memory the agent can read |
constraints | What the agent is explicitly not allowed to do |
A minimal manifest might be 20 lines. A production-grade one for an enterprise deployment might be considerably longer — but the structure stays the same. That consistency is the point.
Why this beats ad-hoc prompts§
Most agent builders start the same way: a system prompt, a tool list, and a loop. It works fine for a demo. It breaks down at scale for a few specific reasons:
- No versioning: You can't diff a prompt the way you can diff a YAML file.
- No auditability: When a regulator or a teammate asks "what can this agent do?", you can't hand them a system prompt and call it documentation.
- No reusability: Agent logic is locked inside one codebase, one deployment, one context.
A manifest solves all three. It's a file. You can commit it, review it, share it, and enforce it.
A Real-World Example: The BeeAI Manifest Format§
One of the clearest public examples of this pattern in action is the BeeAI platform from IBM's open-source team. Their agent manifest spec is YAML-based and covers exactly the fields that matter for production deployments: driver runtime, package or container image, server type, MCP transport, and UI endpoints.
A minimal BeeAI provider manifest looks like this:
manifestVersion: 1
name: github.com/i-am-bee/beeai/agents/community/something
driver: nodejs
package: git+https://github.com/i-am-bee/beeai/agents/community/something
command: ["something", "--transport", "http"]
serverType: http
mcpTransport: sseWhat's notable here isn't the specific fields — it's the philosophy. The manifest is the entry point. You point to it, and the platform knows how to run, manage, and expose the agent. No boilerplate startup code. No runtime configuration buried in a .env file that only one person on the team knows about.
This is infrastructure thinking applied to agents. And it's the direction the whole industry is moving.
Why This Matters Beyond the Code§
The manifest approach isn't just a developer convenience. It has real implications for compliance, collaboration, and scale.
Compliance is becoming unavoidable§
The EU AI Act begins enforcement in 2026 and requires transparency in how AI systems interact with users and handle data. The NIST AI Risk Management Framework is evolving to apply specifically to autonomous agents and tool-using workflows.
If your agent's behavior lives entirely in a system prompt, answering an auditor's question about what it can do becomes a manual exercise. With a manifest, that answer is a file you can share.
"Managed AI agents increasingly must sit inside a formal AI management system with documented policies, KPIs, and risk controls — not ad-hoc scripts." — Industry Expert
Teams can collaborate on agents like they collaborate on code§
When an agent is defined by a manifest file, it becomes a first-class artifact in your development workflow:
- Version it in Git alongside your application code
- Review it in pull requests — anyone on the team can see what changed and why
- Test it against a spec, not just vibes
- Deploy it predictably, because the manifest is the deployment unit
This is how software teams have worked for years. The manifest brings that same discipline to agents.
Reusability across platforms§
A well-designed manifest is also portable. The Agent Communication Protocol (ACP) and emerging standards are converging on manifest-like specifications as the interoperability layer between agent platforms. Build your agent right once, and it can run on multiple runtimes without rewriting the core logic.
Getting Started with Managed Agents on Definable§
At Definable, we built native support for managed agents defined by structured manifests directly into the platform. You can spin up an agent from a text file, connect it to any of our 50+ supported models, wire in tools via our 1,000+ integrations, and deploy it without writing infrastructure code.
The workflow is straightforward:
- Define your manifest — specify the agent's identity, model, tools, and behavioral constraints in a structured YAML or JSON file
- Register it with the platform — point Definable to your manifest and it handles the runtime
- Iterate on the manifest — update the file, redeploy, and your changes are live with a full audit trail
The real value: you spend your time on the agent's logic and capabilities, not on scaffolding and deployment plumbing.
With 35% of organizations expected to broadly adopt AI agents by 2026, the teams that establish clean, auditable agent infrastructure now will be the ones who can scale without rewriting everything from scratch six months later.
If you're ready to move from prototype to production, try building your first managed agent on Definable — it starts with a single file.
