Before deploying AI agents, two things have to be in place: systems the agents can actually reach and act on in real time, and a governance layer that defines who owns what and what the agents are allowed to do. Without interoperable, API-accessible systems and clear accountability, agents have nothing reliable to operate on and no rules to operate within, which is why these are prerequisites rather than features you add later.
The technical prerequisite: systems agents can reach and act on
An AI agent is only as capable as the systems it can read from and write to. If your data lives in formats agents cannot query in real time, or in tools that do not expose programmatic access, the agent cannot complete a task end to end. It can suggest, but it cannot do.
In its report on building the foundation for agentic AI, Bain highlights the need for real-time, API-accessible systems and interoperability standards such as MCP. The point is structural. Agents coordinate across many tools at once, and they need consistent, machine-readable interfaces to move between them. A company that has digitized its records but locked them behind manual exports has not cleared this bar. The work is making each system addressable, current, and connectable so an agent can chain steps without a human stitching the gaps.
The governance prerequisite: ownership, accountability, and limits
The second prerequisite is harder because it is organizational, not technical. Once an agent can act, someone has to be accountable for what it does, and the agent needs explicit boundaries on its authority. Who owns the outcome when an agent sends an email, updates a record, or books a meeting? What is it permitted to do on its own, and where does it escalate to a human?
Bain treats interoperability and governance as prerequisites for agentic AI, not afterthoughts. That framing matters because most failed deployments skip the governance step. They wire an agent into live systems, give it broad access, and discover too late that no one defined the seat the agent fills or the limits on its reach. Governance is what makes agent activity auditable and safe to scale. It turns autonomy from a risk into a controlled capability, with clear owners, clear permissions, and clear escalation paths.
Why both have to come first
These prerequisites reinforce each other. Interoperability without governance produces fast, ungoverned action across your most important systems. Governance without interoperability produces well-defined seats that agents cannot actually staff. You need both before the first agent goes live, because retrofitting either one after deployment means unwinding decisions already in production. Treating them as foundation, not cleanup, is the difference between agents that compound value and agents that compound mistakes.
A practical readiness check is whether you can name, for every agent you plan to deploy, the systems it will touch, the seat it holds on your org chart, the accountability it carries, and the limit on what it may do unsupervised. If you cannot, you are not ready to deploy.
This is exactly what OTP is built to put in place before agents go live. OTP runs your people and AI agents on one org chart where every seat, human or agent, has a named owner and a defined accountability, governed through a structured coordination layer and measured against OTP's 8 Levels of agentic maturity. It is the operating model you run, so the technical and governance prerequisites are not a separate project but the platform itself. See how it works at orgtp.com.