AI Agents Are Becoming Coworkers. Who Is Watching Them Work?

AI agents are becoming the new interface between people and software. The early chatbot pattern was simple: ask a question, receive an answer, decide what to do next. The agentic pattern is different. A user states an intent, and the system plans, calls tools, touches records, asks other agents for help, and sometimes takes action before a person looks again.

That shift sounds like a product feature, but it is really an operating model change. When software starts doing work across systems, companies need to decide what counts as permission, what counts as evidence, and who owns the result when the agent makes a plausible but wrong move.

The hype is ahead of the operating model#

Forrester’s 2026 agentic AI report frames the moment well: agentic AI has reached technical viability, but most organizations are still stuck between promise and payoff. The gap is not just model quality. It is orchestration, control, and what Forrester calls disciplined nonhuman identity.

That last phrase matters. A human employee has a name, a manager, a role, a set of permissions, and a history. An AI agent needs the same kind of containment. Otherwise a company ends up with a swarm of helpful but hard-to-audit actors moving through internal systems.

Enterprise software is already being reshaped#

McKinsey’s analysis of ERP systems shows why this is bigger than productivity tips. Agentic systems can sit above traditional enterprise software and orchestrate processes end to end. Instead of users clicking through screens, agents may become the front end for planning, finance, procurement, HR, and customer operations.

In that world, the ERP system becomes less visible but more important. The underlying records, business rules, and audit trails still need to exist. What changes is the locus of control: people define intent, agents execute more of the process, and humans intervene around exceptions.

This is why “AI agent” is not just another automation label. Robotic process automation followed scripts. Agents infer steps, choose tools, and adapt. That makes them more useful and more dangerous.

The new job: watching the watchers#

The practical question for 2026 is not whether agents can save time. They probably can. The real question is how much autonomy they should receive.

There are at least four layers to watch:

1. Identity: Every agent needs a durable identity, limited permissions, and clear ownership.
2. Memory: What can the agent remember, for how long, and from which systems?
3. Action: Which actions require approval, and which can run automatically?
4. Audit: Can a human reconstruct why the agent made a decision?

Without those layers, companies may create a strange new form of shadow IT: not unsanctioned apps, but unsupervised digital workers.

Why this matters outside the enterprise#

Agentic AI will not stay inside corporate back offices. The same pattern will appear in personal productivity tools, browsers, shopping assistants, health apps, tax software, and education platforms. A consumer agent that can book travel, compare plans, negotiate prices, or submit forms will be convenient. It will also need access to sensitive data and the authority to act.

That means agent design is becoming a public trust issue. People will need simple ways to answer: What did this agent do? What did it see? What can I undo? Who is responsible if it fails?

My take#

The most useful agent products will not be the ones with the flashiest autonomy. They will be the ones with the clearest boundaries. A good agent should feel less like magic and more like a reliable junior coworker: capable, fast, but always visible enough to supervise.

The next AI race may be less about who can build the smartest agent and more about who can build the most governable one.

Sources#

• Forrester: The State Of Agentic AI, 2026
• McKinsey: The end of ERP as we know it? Five ways AI is disrupting ERP