AI Workflows & Agentic AI

From Automating Tasks to Pursuing Goals

AI can now do much more than answer questions. It can retrieve information, create content, automate multi-step processes, monitor changing conditions, interact with systems, perform tasks, and increasingly, pursue defined goals.

But not every job requires the same level of intelligence or autonomy. Sometimes a structured workflow is exactly what you need. Other times, AI needs the flexibility to determine what steps are required to accomplish a goal.

Understanding that difference is the starting point.

AI Workflows:

When the Process is Known

AI Workflows connect repeatable steps across information, systems, and people. They are designed for work where the process is understood: retrieve this information, analyze it, create an output, route it for approval, update another system, or trigger the next step.

Workflows can combine Generative AI, LLMs, traditional automation, business rules, enterprise software, and other technologies. Their strength is consistency: the process is defined, and the technology helps execute it faster and with less manual effort.

AI Workflows can support everything from document processing, reporting, onboarding, and information retrieval to customer support, approvals, data entry, compliance, and multi-step business processes.

With AI Workflows, businesses can:

Connect structured and unstructured information into a single process

Automate recurring, multi-step work across people, systems, and departments
Increase speed and throughput while maintaining consistency and accuracy
Reduce repetitive work so people can focus where their judgment and experience matter
Connect each step into a coordinated end-to-end process

A Workflow Follows a Process. An Agent Pursues a Goal.

AI Agents

The BUilding Blocks of Agentic AI

Unlike a predefined workflow, an AI agent can be given a goal and determine what actions are needed to accomplish it within defined boundaries. It can gather information, use tools, interact with systems, monitor changing conditions, perform tasks, and adapt as circumstances change.

Agents can use different forms of intelligence depending on the job. An agent may use an LLM to understand and communicate, enterprise systems to retrieve or update information, Data AI to recognize patterns, and Causal AI when deeper reasoning, simulation, or cause-and-effect understanding is required.

The important distinction is not the technology inside the agent. It is the agent's ability to pursue an objective, determine the next appropriate steps, and act within the authority it has been given.

Imagine AI Specialists Working Alongside Your Team

Agents can be configured around specific roles and responsibilities, using the knowledge, information, and systems needed to perform their work.

Procurement Agent
Monitors suppliers and commitments, gathers information, evaluates options, and supports sourcing activities.

Supply Chain Agent
Monitors inventory, materials, capacity, supplier commitments, schedules, and changing conditions across the supply chain.

Finance Agent
Gathers and analyzes financial information, monitors changing conditions, and supports planning and decision-making.

Compliance Agent
Reviews requirements, documentation, and changing conditions and flags potential issues that require attention.

Knowledge Agent
Makes organizational knowledge, procedures, lessons learned, and expert guidance available when and where employees need it.

Operations Agent
Monitors operating conditions, identifies exceptions, gathers relevant information, and helps coordinate appropriate responses.

These are examples, not predefined products. Agents can be configured around the roles, knowledge, systems, processes, and work that matter to your organization.

What Is Agentic AI?

When AI Can Plan, Act and Adapt

Agentic AI takes AI beyond responding to individual prompts. It enables AI agents to work toward defined objectives, determine what steps are needed, use available tools and information, take authorized actions, evaluate what happened, and adapt as conditions change.

An Agentic AI solution can involve a single agent or multiple specialized agents working together.

Imagine a supply chain agent identifying a material shortage. A procurement agent evaluates alternative suppliers. A finance agent assesses the cost implications. An operations agent determines the effect on production.

Each agent brings specialized knowledge and capabilities while sharing the context needed to address the larger business problem.

The result is not simply more automation. It is intelligence that can act, adapt, and coordinate work across traditional boundaries.

From Agentic AI to Digital Co-Workers

AI That Works Alongside Your People

The larger opportunity is not simply replacing work with autonomous technology. It is giving people intelligent co-workers that can work alongside them.

A digital co-worker can bring together organizational knowledge, enterprise data, AI agents, workflows, Generative AI, and, when deeper reasoning is required, Causal AI. It can monitor changing conditions, gather and analyze information, perform authorized tasks, prepare recommendations, and explain the evidence and reasoning behind them.

And when a decision requires human judgment, the digital co-worker can bring the issue to the right person with the information needed to act.

People remain in control of the decisions that matter. AI helps them get there faster and better informed.

The Right Level of Intelligence for the Work

Sometimes the answer is a simple automated workflow. Sometimes an AI agent needs to perform tasks and adapt along the way. Sometimes multiple agents need to coordinate. And sometimes the problem requires deeper causal reasoning, simulations, and human decision-making.

The goal isn't to make everything agentic. It's to apply the right level of intelligence and autonomy to the work that needs to be done.

Automate What Should Be Automated. Augment What Should Remain Human.