Across the enterprise landscape, automation is evolving at a remarkable pace.

Large language models (LLMs) have moved from academic research to the center of business conversations, reshaping how leaders think about workflows, operations, and the future of work.

Yet the promise of automation goes beyond generating text or drafting emails. Real transformation happens when AI can take action: when systems understand goals, make decisions, and complete processes that span multiple systems, approvals, and data streams.

At Automation Anywhere, we call this shift agentic process automation (APA). And at the core of APA is a capability we’ve been building for years: the Process Reasoning Engine (PRE). PRE serves as the intelligence layer that allows agents to orchestrate, decide, and improve. It is the brain of agentic automation.

Why Enterprises Need More Than LLMs

Automation has been part of enterprise technology for decades. Scripted workflows, robotic process automation (RPA), and machine learning models each delivered efficiency, but they also had limits. LLMs expanded the field dramatically, enabling tasks like classification, summarization, natural language interaction, and now even reasoning without extensive training data.

Enterprises, however, operate in an environment where accuracy, trust, and scalability carry more weight than novelty. A CIO will not hand over a mission-critical process to a system that can generate fluent text but struggles with consistency. Automation must be reliable, auditable, and tailored to the context of each organization.

LLMs are powerful pattern recognizers, but they lack the contextual awareness and ability to take specific action that enterprises need. PRE provides that missing layer. It ensures that agents act with clarity, reliability, and accountability, transforming raw generative capability into outcomes businesses can trust.

What the Process Reasoning Engine Does

PRE transforms raw generative capability into enterprise-grade execution in three essential ways.
 

1. Clear goals and action plans

Agents begin with a definition of their task: the outcome they are driving toward, the systems they can access, and the tools (automations, APIs, Document Automation solutions, etc) available. PRE translates that definition into a structured action plan. This planning step moves agents from vague prompting to deliberate execution.
 

2. Orchestration across systems and agents

Enterprise processes rarely involve a single tool. PRE coordinates actions across automations, APIs, other agents, and human checkpoints. It decides when to move forward, when to retry, and when escalation is required. Orchestration is what enables an agent to complete a process end-to-end rather than handing back partial results.
 

3. Continuous learning from feedback
Every enterprise seeks improvement. PRE incorporates corrections, feedback, and eval results into future runs. Over time, agents become more accurate, more resilient, and more aligned with the unique context of the business.

When these three elements combine, agents deliver not only outputs but also measurable business results.

PRE in Action: An Invoice Processing Example

Consider the task of handling vendor invoices. In earlier generations of automation, enterprises might train models to classify invoices and extract fields. Success required large labeled datasets and structured document formats, and even then, exception handling consumed significant human effort.

PRE changes the equation. Imagine a workflow where:

• The system monitors an inbox and ingests an invoice and the accompanying email.
• The invoice processing agent is invoked, which has a series of tools available to it in order to accomplish its goal of accurately processing incoming emails and attached invoices.
• The agent classifies the incoming documents before calling its Document Automation tool - which parses the layout, interprets tables, and extracts the key fields.
• It compares details in the email with the attached invoice, identifying discrepancies such as corrections or clarifications.
• It validates the extracted data against a system of record to confirm supplier IDs and purchase order references.
• For most incoming invoices, that means validated, straight-through processing. But if uncertainty remains, the agent knows when to escalate to a human decision-maker before it completes processing, and applies that learning in the future.

This approach delivers resilience. The agent adapts to variable formats, integrates multiple sources of information, and improves with each run. What once required brittle scripting now operates as a dynamic, context-aware process.
 

Governance: From Demo to Production

In my years building AI systems, one truth has become clear: the challenge is not producing a compelling demo. The challenge is building dynamic AI systems that can have predictability in a production environment.

PRE is designed with that reality in mind. Governance is built into its core:

• Observability. Every action, prompt, and decision is recorded for full auditability.
• Evals. Enterprises can test agents against realistic scenarios before deployment and continue measuring them in production.
• Data security. Role-based access controls and data masking protect sensitive information at every step.
• Escalation. When ambiguity arises, agents route decisions to humans in a controlled, traceable way.

These safeguards turn automation from a proof-of-concept into a dependable part of enterprise operations.
 

Beyond Invoices: PRE Across the Platform

Goal-oriented, outcome-driven agents and invoice processing highlight just some of what PRE can do, but its impact spans the Automation Anywhere platform.

• Agent orchestration. PRE guides goal-based agents, determining which tools to use and how to sequence them.
• Developer acceleration. Builders can describe desired outcomes in natural language, and PRE translates that intent into automation logic.
• User empowerment. Business users can state an intent — for example, “I have a claims exception I need to process.” PRE understands that context, calling the appropriate agents and automations to resolve the users challenge.
• Document automation. PRE powers intelligent document processing, combining vision models, layout understanding, and feedback loops for higher accuracy.
• Enterprise UI Agents. Trained on millions of automation runs, these agents can navigate applications and websites even when there are significant layout variations
• Generative Recorder. Application layouts change, but that doesn’t mean your UI-based automations will fail. Generative Recorder understands application layouts and automation context that make for reliable, self-healing agents.

In each scenario, PRE provides reasoning, context, and learning, giving agents the enterprise awareness needed to move from simple execution to true business outcomes.
 

The Future: Expanding Autonomy

As enterprises deepen their use of agentic automation, I see three directions where PRE will play an increasingly critical role.

A new balance between deterministic and adaptive logic
Today, most processes remain largely scripted, with small segments handled by adaptive agents. That balance will shift. As confidence in agent performance grows, more decision points will transition from rigid logic to adaptive reasoning.

On-demand composition of agents
Many workflows will remain predefined, but some tasks can be created dynamically. With PRE, an employee could request an outcome in natural language — such as reconciling datasets or validating supplier information — and the system could compose a temporary agent equipped with the necessary tools.

Systems that improve continuously
Evals already enable organizations to measure agent performance. The next step is using those signals as input for improvement. PRE will allow agents to learn directly from evaluation outcomes, creating a cycle of refinement that moves faster than manual reprogramming ever could.
 

Lessons From Building PRE

Three lessons stand out from our journey so far.

• Autonomy works best with strong guardrails. Giving agents decision-making power creates value, but only when supported by governance, observability, and security.
• Context drives performance. Generic models fall short in enterprise settings. PRE succeeds because it brings context from systems, data, and feedback into each decision.
• Improvement is continuous. Agents evolve through constant testing and iteration. PRE provides the structure for that evolution.

These lessons will shape how enterprises across industries adopt AI agents in the years to come.
 

The Brain That Makes Agents Real

Enterprises are looking for more than prototypes. They need automation that scales reliably, produces measurable outcomes, and integrates with the systems they already depend on.

The Process Reasoning Engine enables that future. By adding reasoning, orchestration, and learning, PRE transforms generative models into automation that enterprises can trust. It serves as the hidden brain of agentic automation - the layer that allows agents to act with intelligence rather than simply generate outputs.

Agentic automation enables organizations to handle complexity, adapt to changing conditions, and free their people to focus on higher-value work. PRE makes that vision real today and lays the foundation for what comes next.