The next AI agent no longer waits for your prompt

The first generation of generative AI answered questions. The next executed individual tools. Today agents can already research, edit files, operate programmes, produce reports or pursue a goal across several work steps.

Mostly this still starts with a clear assignment. A human formulates the goal, the agent plans the steps and works towards it. The next step in the development is that the system no longer waits for that explicit goal.

When the system identifies the need to act

A proactive agent does not only observe single tasks. It recognises connections over time. It sees that a project repeatedly produces the same delay, that a process regularly fails at a handover or that sales figures are declining in a particular region.

It can then prepare a report, produce a root cause analysis, inform those responsible or work out a proposed solution. The work begins before anyone writes a prompt.

For companies that is attractive. Problems are spotted earlier, routines are relieved and information is available faster. But proactivity can also create a new form of uncontrolled activity.

Activity is not yet value

An agent can work in a technically correct way and still miss the actual need. It can produce a report nobody needs, trigger an escalation although the matter is already settled, or optimise a process whose goal has since changed.

The more autonomously a system acts, the more important boundaries become. It must not only know what it can do. It must understand when to wait, to ask back or to stop.

Autonomy without decision boundaries is not productivity. It is uncontrolled activity.

Four conditions for proactive agents

Context. The agent needs current information about goals, projects, roles and dependencies.

Accountability. It must recognise who is allowed to decide and who merely needs to be informed.

Justification. Every proactive action must be traceable back to trigger, data and rules.

Intervention. People must be able to stop, change or reject actions.

These conditions are not add-on features. They are the precondition for an agent to be allowed to act inside a company. Without them you get a system that does a lot but cannot reliably tell important, urgent and merely conspicuous apart.

From assistant to action layer

Agents do not replace leadership. They form a controlled action layer between organisational knowledge, process and execution — and a company has to design that layer deliberately.

An agent can recognise that a decision is missing, gather the relevant information and prepare a next step. But it must not define which corporate goal matters more or which risks should be accepted.

The organisation defines which actions may happen automatically, which require approval and which always stay with people. Exactly that definition is leadership work, not technical configuration. It is the part where I support companies.

Today's agent waits for a goal. The next one recognises for itself that a goal is missing. What matters is who turns that into an assignment.

This is the second of three articles on AI agents in the enterprise. The first was about what happens when AI infers intent. The third about why agents stay blind without an organisational memory.