Real Agents Take Action

By Peter White, SVP of Product Management, Automation Anywhere

Agentic Confusion

Scroll through LinkedIn or attend an AI conference, and you’ll hear dozens of definitions of what an “agent” is. Some describe any chatbot that answers questions. Others point to plugins or integrations that extend model capabilities. The result is a cloud of confusion where everything becomes an agent and, at the same time, nothing does.

For enterprises, this lack of clarity creates real risk. Decision-makers evaluating technology need to know what agents can deliver today, how they differ from other forms of automation, and what criteria separate hype from substance.

From my perspective leading emerging product strategy at Automation Anywhere, one principle has consistently proven true: agents earn the name only when they combine knowledge with actions that drive outcomes.

Defining an Agent

At its simplest, an agent combines three capabilities:

1. Autonomy. It makes decisions without constant human intervention.
2. Reasoning. It applies context, weighing ambiguous inputs to determine a course of action.
3. Execution. It connects reasoning to tools, completing steps that achieve an outcome.

Remove any one of these, and the system falls short of being an agent. An LLM-based assistant that drafts text or answers questions about a document is a helpful tool, but not an agent. A deterministic workflow that follows rigid rules without adaptation is automation, not agency.

In practice, agents exist on a spectrum of autonomy.

• Goal-based agents. At the high end are agents that can pursue open-ended goals using multiple tools and pathways. Given a target outcome, they decide which steps to take, adapt as conditions change, and persist until they reach completion or escalate.
• Decision-making agents. In the middle are agents that choose among predefined outcomes. They may use generative models to resolve ambiguity and then act, such as deciding how to route a support ticket or classify an incoming document.
• Assistive agents. On the lower end are tools that provide suggestions or partial outputs but stop short of execution.

Each tier has value, but only those that connect reasoning with action earn the title of agent in an enterprise setting.

Why Action Matters

Conversation is easy. Action is harder.

When an agent acts, it crosses a threshold that changes enterprise impact. Execution means connecting to systems of record, invoking automations, triggering approvals, or updating databases. It transforms abstract intelligence into tangible outcomes.

This matters for three reasons:

• Business value. Outcomes, not conversations, deliver measurable ROI.
• Complexity management. Agents reduce the need for brittle handoffs across fragmented systems.
• Trust and accountability. Actions can be audited, tested, and improved in ways that conversations alone cannot.

Enterprises should demand this standard. Agents that act are the ones that justify serious investment.

Consider a common workflow: handling incoming customer emails.

A conversational system can summarize the message or suggest draft responses. Useful, but incomplete. An agent, guided by PRE, goes further:

• It classifies the email into categories such as billing, technical support, or product inquiry.
• It extracts relevant details and validates them against systems of record.
• It triggers the appropriate automation process — creating a support ticket, initiating a refund, or addressing general product questions - all of which could be processes on their own, represented as a blending of other AI agents as well as deterministic processes.
• Even in situations where it needs to escalate to an account manager, it learns from corrections when a human reclassifies or adjusts the case.

This shift from suggestion to execution closes the loop. The enterprise gains efficiency, accuracy, and speed.

Guardrails for Action

Action creates value, but it also raises the stakes. Enterprises need confidence that agents act appropriately, securely, and transparently.

That is why governance is inseparable from agency:

• Auditability. Every action must be logged, from the decision point to the system call.
• Evals. Agents should be tested across scenarios before deployment and continuously monitored afterward.
• Access controls. Agents operate only within the permissions defined by role-based governance.
• Escalation paths. When an agent reaches the limits of its reasoning, it should hand off clearly to a human.

These safeguards make action not only possible, but dependable.

The Market Noise vs. Enterprise Reality

The current hype cycle blurs lines. Every chatbot demo becomes an “agent,” every LLM integration gets labeled as “agentic.” For enterprises, this is more than semantics. Adopting immature definitions risks wasted investment and stalled projects.

By contrast, organizations that focus on agents with real action capability build a stronger foundation. They measure success not by novelty, but by outcomes. They look for systems that integrate reasoning with orchestration, governance, and improvement.

The coming year will see more enterprises deploying agents with meaningful autonomy. Early pilots will move into production, and definitions will sharpen under the pressure of real-world use cases.

The standard that emerges will be clear: agents act. They reason about context, decide on a course, and execute steps that achieve results. Everything else, from assistants to chatbots to scripts, has value, but is not fully agentic.

Agents that act will drive the next wave of transformation. They will orchestrate processes across systems, manage ambiguity, and improve through feedback. And with the right governance in place, they will do so safely, securely, and at scale.

That is the standard we are building toward at Automation Anywhere. And it is an approach that will reshape work in every enterprise.