The age of AI is well upon us. According to research by Microsoft, 24.7% of the working age population in the Global North is using AI, paired with 14.1% of the Global South. As AI adoption increases, organizations are increasingly finding their minds focused on not just the potential upside of AI, but on the realistic steps and processes that must be implemented in order to be truly agentic.

Today, we’re going to look at the path that adoptees need to take to ensure strong utility, effective implementation, and efficient use of resources in the agentic AI era. We’ll look at exactly what is required for an enterprise to be positioned for agentic AI, and what you might lose by failing to be proactive today.

Why Proactivity Is Best

AI is here to stay. While it may change form and function rapidly in the next few years, it’s proving valuable in appropriate use cases. While AI doesn’t necessarily make sense integrated everywhere, there are many implementations where AI is a strong value addition across industries, from healthcare to finance, industrial manufacturing, energy, and beyond.

Accordingly, it’s less about if AI will come to your organization and more about how you can make the most of it when it arrives. As such, being proactive here is the best approach. Many of the processes you put in place today to prepare for agentic AI are helpful practices for today’s problems as well as for the future’s problems. So, at worst, you come out of this with a better platform, and at best, you have a framework to properly reap all the benefits of an agentic future.

With this in mind, let’s dive into the path itself.

1. Implement Proper Identity Controls

The first, and arguably the most important stage of this process, is to implement proper identity controls. The reality is that we are moving away from human-only identity and must consider non-human identities as well. Even if you abstract away AI as an offering, there are still workload attestation mechanisms at play, automation scripting, and much more that require something more specific and well-suited.

We can see this need in the evolution of how identity has been handled over the decades. What was once a series of shared root passwords evolved to named users with privileged access classes. This then moved further into workload attestation for machine identities. The reality is that much of the traffic we handle with APIs and applications is no longer humans behind a screen, and as such, our identity management must be built to accommodate that.

In the age of AI, this is complicated somewhat by the tendency to vibe code implementations. We see developers hard-code access credentials in the flow or share login tokens to make the application work. The fact is that this approach comes from a fundamental misunderstanding of AI workflows and reflects a failure to treat identity as a first-class concern.

2. Rework Your Authentication and Authorization to be Limited in Time and Scope

Another huge step in getting this right is shifting your authorization patterns away from long-lived solutions that simply grant access and then move on. In traditional systems, you might have one user that requests permissions and then predictably leverages the system for a long time.

In agentic and automated systems, however, you don’t have this — you have agents that are ephemeral in nature, existing only as long as it takes to accomplish their specific task in their given environment. Instead of “Andrea from Accounting,” you have “Agent 2019 working in Billing temporarily.”

The kicker here is that this is mostly true even absent AI. Automated systems may only temporarily need to connect, or may only connect once in a set interval, needing short-lived credentials. For many orgs, however, their systems are still set up to be longer-lived, creating a tension for short-lived workloads and tasks.

To support this kind of auth flow, enterprises must move towards a strong binding of workload to identity and then to role-based access controls (RBAC). By assigning an agent an RBAC profile and limiting the lifetime of this access according to their identity attestation, you can create policy-driven and ephemeral authentication and authorization that are limited in time and scope and can be rapidly revoked.

Instead of “This agent can access the accounting API,” the goal should be “This agent can access the payment status service of the accounting API only while executing within an authorized environment for the duration of a prompted interaction (at a maximum of 30 minutes).”

3. Implement Just-in-Time API Access

Autonomous agents are becoming increasingly common, and they represent a separate but secondary utilization paradigm compared to other AI agents and automated flows. With typical agent flows, short-lived access is fine. But for these autonomous systems that have no human behind the prompting, or at least are so far removed from the chain of prompting that there might as well not be a human involved, a more controlled, ephemeral system is required.

The solution is just-in-time API access. The idea borrows heavily from zero-trust solutions and essentially requires the agent to earn access per action. The system in place would assume that the agent making the request is suspicious from moment one, and that compromised data flows or hallucinations are likely outcomes.

The problem is that typical AI agents at least have a human behind the prompt, and a good deal of the error correction can happen via the requester. For truly agentic flows, however, this is not true. Agents can hallucinate, tool schemas can drift, model upgrades can immediately change behavior, and even a trusted autonomous agent can suddenly go haywire. In this reality, APIs issue tokens for just-in-time access that is highly scoped (such as one API and one action per token), time-bounded, and set to auto-expire without the chance to renew.

This sort of system can secure truly autonomous flows, making each step validate its need for access and preventing privilege escalation.

4. Eschew Custom and Proprietary for Open Standards

As agents proliferate, the ways that people interact with and leverage them also start to stack up. As a result, there is a good deal of competition between open and proprietary standards. Enterprises may see value in proprietary solutions, especially when they’re given flashy sales decks, but the reality is that open standards will position you in a much stronger place for future agentic development.

Accordingly, enterprises should lean into standards-based development and automation integration, especially when it comes to the systems underpinning security and interconnection. For example, OAuth 2.0 has proven effective for decades. Model Context Protocol (MCP) allows us to connect tools and resources in a highly accessible way.

The reality is that it’s not good enough to just have good scaling or extensibility. You need to make sure that the tech underpinning that scaling and extensibility is itself flexible and expandable. We’ve seen this game play out again and again. While Adobe Flash was the king of web animations for a long time, it’s been overtaken by the open HTML5 standard. Similarly, HomeRF disappeared as a wireless internet solution while Wi-Fi (IEEE 802.11) grew to universal dominance.

5. Adopt Human-in-the-Loop as a Control Plane

Even with the best systems in place, you are still likely to have gaps in your processes. In order to deal with these, enterprises should adopt human-in-the-loop (HITL) as a true control plane.

Enterprises often treat HITL as a checkbox that is operator-centric, as in “ask for approval before doing something risky.” But that’s just not enough. Autonomous AI agents can flood this sort of solution, resulting in alert fatigue and ineffective controls. Instead, an HITL process should be policy-triggered and based on risk thresholds, as well as tightly integrated with identity and auth patterns.

As an example, agentic flows accessing a public API and grabbing public data should not be something suspect. That is something that an external user could easily do, and triggering an alert for HITL processing would be a waste of resources and attention.

But what if the agent tries to escalate its own privileges? What if it crosses data sensitivity boundaries even if its auth pattern should prevent this? What if it tries to do something outside the original prompt, clearly bypassing both its limitations and the prompt requirements? These sorts of actions should trigger the HITL process. In essence, humans should be governors, not just operators, and should be the true arbiter of such a process.

6. Add Context and Observability for Improved Accountability

Autonomous agents need to be tracked to ensure there is context for observation and control. Every action should answer who the agent is, what they are trying to do, under what identity, and with what authorization. Going further, organizations should understand on whose behalf the agent is acting and for what goal.

Autonomous agents must be auditable and compliant, but they must also be formed for model accuracy, data quality, and safety considerations outside of just efficiency and model improvement. Accordingly, systems must be built intentionally around tracking these signals throughout every stage of their request.

Importantly, this context should be baked into the very core functionality of the agents. Nothing agentic should happen on your platform without these pieces of information as metadata. But pointedly, this should also be true whether the request is agentic or not.

Looking Toward the Future of Agentic API Consumption

The reality is that much of what’s on this list is best practice for interconnected enterprise systems in general, let alone those touched by agentic solutions.

And this really exposes a critical truth of the modern era: those who keep up to date and aligned with current best practices as well as emerging strategies at the frontier edge, are going to be well-positioned for the agentic future. Those who decide to languish with old standards and practices will fall behind — autonomous agents or not.