AI Workflows: Multi-Step Automation

Turning Data and Tasks Into Continuous, Automated Flows

As we introduced on the Our Solutions page, workflows connect steps across tasks. Here we take a closer look at how they operate in practice.

AI Workflows move data from one action to the next without manual handoffs. They go beyond single-task automation by linking actions such as retrieving data, analyzing trends, generating outputs, and personalizing results.

These workflows can be powered by large language models (LLMs), smaller task-focused models (SLMs), or multiple specialized models working together for greater efficiency. Business rules, logic, and sequencing are built into each step to keep processes accurate, consistent, and aligned with organizational goals.

Why Workflows Matter

Workflows mirror how work actually happens inside a business. They reduce manual effort, enforce consistency, and accelerate throughput so teams can focus on higher-value activities.

With AI Workflows, businesses can:

Integrate structured and unstructured data into one process
Automate recurring, multi-step tasks across departments
Maintain accuracy while increasing speed and throughput
Free teams from repetitive work so they can focus on higher-value activities
Link every step into one connected process

Workflows are powerful, but they follow predefined rules. To go deeper, we need to look at agents.

Link every step into one fast, connected process.

What Makes an AI Solution a Real AI Agent?

Moving From Insights to Independent Action

Unlike workflows, agents are not locked into predefined steps. They adapt.

AI is no longer just about responding to prompts or automating routine tasks. Real AI agents represent the next evolution. They are systems that can learn, reason, act independently, and work toward defined goals.

In short, agents go beyond assistance. They can take action, make decisions or recommendations, and clearly explain the reasoning behind them

True Agents Have:

The Ability to Learn: Acquire knowledge from people, data, or their own experiences, improving over time.
The Ability to Predict: Anticipate likely outcomes, simulate alternatives, and explore “what if?” scenarios.
The Ability to Reason and Decide: Apply logic, weigh constraints, and take autonomous action to achieve a goal (or KPI).
The Ability to Explain Themselves: Justify recommendations so human colleagues understand the “why,” not just the “what.”

Currently, the only form of AI that delivers all of these capabilities is Causal AI. By combining expert-taught knowledge with real-world data, Causal AI meets the full definition of a true agent, learning, predicting, reasoning, deciding, and explaining in ways that improve both decision speed and decision quality.

The term “agent” comes from the concept of human agency: the capacity to act independently and make choices that shape outcomes.

When decisions can be explained, they can be trusted and validated. Workflows move tasks forward, but agents move decisions forward.

When decisions can be explained, they can more easily be validated.

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How Do Agents Work Inside a Business?

Once you understand what makes an AI solution a true agent, the next question is how they actually operate in the real world. Inside a business, each agent takes on a defined role, just like a specialist on your team.

Procurement Agent: Simulates supplier scenarios, balances cost and risk, understands how each product is made, tracks quality, and recommends who to buy from based on usage, yield, and total cost of ownership.
Finance Agent: Analyzes cash flow, monitors risk exposure, and runs “what-if” scenarios for investments, budgets, or credit decisions.
Supply Chain Agent: Tracks inventory levels, predicts shortages, and recommends adjustments to procurement or logistics to keep deliveries on schedule.
Compliance Agent: Monitors regulations, checks documentation, and flags risks to ensure operations stay aligned with legal and industry standards.
Risk Management Agent: Assesses churn, market volatility, or operational exposures and recommends mitigation steps.
Maintenance Agent: Monitors equipment health, predicts failures, and schedules service before downtime occurs.
Connected Worker Agent: Guides employees in real time with instructions, safety alerts, and best practices, ensuring frontline teams can act confidently and consistently.

Agents do not work in isolation. They communicate with each other, share context, and escalate when needed. For example, a supply chain agent can update a finance agent on shipping delays, which prompts the procurement agent to recommend alternative suppliers. Over time, these interactions form a connected system that helps your business respond faster, understand more deeply, and act with greater confidence while keeping effort low and impact high.

This interconnected approach is what we call Agentic AI, a digital workforce of explainable agents working in unison with your team

When expertise works together, the results speak for themselves.

Imagine AI Specialists Working With You Workforce

How Your Workforce Interacts With an Agent

Amplifying the Workforce Experience

When a problem arises in a traditional business environment, whether it is a production delay, a pricing conflict, or a drop in customer satisfaction, the burden falls on employees to manually:

Recognize the issue
Determine what data is needed
Collect and clean that data
Analyze it across systems or spreadsheets
Participate in challenge sessions to refine and validate recommendations
Propose a solution

From there, they wait for alignment, debate options, or escalate the issue. If new questions arise, parts of the process are repeated. This creates a slow, reactive, and often incomplete response. By the time the organization acts, value has already slipped away.

What Happens With an Agent in Place?

From Burden to Intelligent Action

Now imagine that same scenario with a true intelligent agent involved.

Instead of a series of steps handled by different people over hours or days, the agent acts in one continuous flow:

Detects the issue in real time
Gathers the right data instantly
Evaluates upstream and downstream impacts
Tests scenarios against KPIs
Applies reasoning using causal knowledge
Recommends the best course of action
Explains the “why” behind the recommendation

Your team no longer needs to chase information or interpret fragmented signals. The agent brings the data together, explains the issue, and delivers a clear recommendation. Time is not lost; time and value are gained. Your workforce stays in control, validating recommendations, applying judgment, and making the final decisions while the agent removes the busywork and accelerates the process.

No dashboards to build. No spreadsheets to reconcile. No guesswork.

Only intelligent action at speed.

What is Agentic AI?

A Smarter, Connected System of Agents

Agentic AI connects multiple intelligent agents into a collaborative system. Each agent focuses on a specific area, while agencies coordinate across many agents to ensure alignment and shared goals. This creates a digital structure that mirrors how organizations already operate, only with greater speed, consistency, and intelligence.

Together, agents:

Work across business functions
Share data, goals, and reasoning
Escalate to human colleagues when needed
Continuously improve outcomes with every cycle

Picture a supply chain agent updating a finance agent, who then informs a procurement agent, who delivers real-time adjustments to vendors, all without manual coordination.

This is Agentic AI in practice: a digital workforce of explainable agents that partner with your teams to deliver faster answers, deeper context, and more confident decisions. Just like a good co-worker should.

Learn More About Agentic AI

The Difference Between Agents & AI Workflows

Workflows Move Data. Agents Move Decisions.

AI Workflows
AI workflows are ideal for automating routine, repeatable tasks. They:

Pull and organize data
Follow predefined steps set by people
Deliver consistent results with speed and accuracy

Think of workflows like assembly lines. Each step is mapped in advance: “Do this first, then that, then send a result.” They reduce busywork and keep processes moving, but they do not think on their own.

AI Agents

Agents work differently. They are more like experienced teammates who:

Understand the bigger picture
Weigh options and constraints
Recommend the best next move based on goals and KPIs
Explain the reasoning behind their recommendations

Agents do not just move tasks forward. They move decisions forward. They adapt when circumstances change, pursue defined objectives, and help your team act with confidence.