What Are Agentic Workflows?

Workflows have come a long way from the days of static checklists and manual task routing. Automation has helped teams move more quickly by taking repetitive work off their plates, but it still relies on people to tell the system what to do, when to do it, and how. That’s changing.

Enter agentic workflows. These AI-powered workflows can carry out tasks, adapt to changes, and move toward goals without constant oversight. They understand context, make decisions, and learn over time, freeing up teams to focus on work where they’re most valuable.

In this article, we’ll explore how agentic workflows work, where they’re being used, and what teams should know before adopting them. Whether you're in marketing, customer support, finance, or operations, this guide will help you understand what’s changing and how to stay ahead of it.

Understanding agentic workflows

An agentic workflow is a goal-driven process powered by artificial intelligence that can take action, make decisions, and adapt without constant direction from a person. These workflows rely on intelligent systems to understand context, respond to changing inputs, and continuously improve.

Unlike traditional automation, which follows static rules, agentic workflows are dynamic. They’re built to handle uncertainty and complexity, often working across systems or teams. Instead of executing a predefined series of steps, they determine how to reach an outcome based on available data, goals, and prior experience.

Key characteristics of agentic workflows:

• Decision-making autonomy. Agents assess options and choose a path forward on their own.
• Learning and iteration. Feedback helps refine future actions.
• Context awareness. The workflow adjusts based on data, behavior, or environmental cues.
• Human collaboration. Agents can escalate, ask for input, or share updates as needed.

Since they adapt to context, learn from feedback, and work toward goals independently, agentic workflows are especially valuable for teams juggling high-volume, high-variability work, where rigid rules can’t keep up with real-world complexity.

Types of agentic workflows

Not all agentic workflows operate the same way. Some act independently from start to finish, while others support teams by surfacing insights, proposing actions, or responding to events as they happen. The type of agentic workflow you use depends on the role it’s meant to play and how much autonomy your team is comfortable handing over. 

Here are four common agentic workflows:

Autonomous workflows

These run with little to no human input. Once a goal is defined, the agent makes decisions, carries out tasks, and learns from results. For example, an autonomous workflow could monitor incoming payments, reconcile records across systems, and flag discrepancies—all without intervention.

Collaborative workflows

In these setups, agents and people work together. The agent might draft a response, suggest next steps, or flag risks, while the person approves, edits, or adds context. Collaborative workflows are common in marketing, customer support, and planning scenarios, where nuance or brand tone still matters.

Reactive workflows

These workflows respond to specific events, like a sudden spike in support tickets or a drop in web traffic. The agent detects the trigger, analyzes the situation, and initiates a predefined or adaptive response.

Proactive workflows

Rather than waiting for a prompt, proactive agents look ahead. They might detect early signs of churn, recommend reallocating budget, or suggest scheduling maintenance before issues occur, helping teams act before problems grow.

Most real-world agentic workflows combine elements of each. The right approach depends on the process, the data available, and how much control your team wants to keep at each stage.

Building blocks of agentic workflows

Agentic workflows are systems designed to understand goals, interpret information, take action, and learn over time. While each setup is different, most are built from two distinct layers: foundation capabilities that make intelligence possible, and system components that organize and apply those capabilities in practice.

Foundational capabilities

These foundational technologies give agentic workflows the intelligence and flexibility to operate in complex environments:

• Machine learning (ML): Identifies patterns in historical data and helps workflows apply those insights to new situations. While often grouped with AI, AI and machine learning play distinct roles. ML powers the learning; AI supports goal-based decisions.

• Natural language processing (NLP): Enables workflows to interpret unstructured text like emails, feedback, or support tickets—essential for real-time understanding and communication.

• Robotic process automation (RPA): Supports rule-based actions like data transfers or alerts, especially where systems don’t support AI natively.

System components

These parts work together to move work forward, surface insights, and adapt as needed:

• AI agents: Autonomous software entities that act toward specific goals, often coordinating across systems or with people.

• Large language models (LLMs): A central part of many AI models, LLMs help agents reason, summarize, and operate in nuanced or ambiguous situations.

• Prompt engineering: Prompts shape how agents respond. Well-structured prompts help ensure workflows align with goals and context.

• Feedback mechanisms: Agents improve over time by learning from outcomes, human input, or system data.
• Multi-agent collaboration: Complex workflows often involve multiple agents working together, gathering data, generating outputs, and handing off context.

• Integrations and tools: Agents should have access to systems teams already use, like data sources, dashboards, and messaging platforms, to take action where the work happens.

Together, these technologies and components create workflows that go beyond automation, helping teams make progress with less effort and more adaptability.

How an agentic workflow runs

Agentic workflows function as intelligent, adaptive loops. They don’t just follow instructions; they interpret context, make decisions, take action, and improve over time. Each stage is focused on progressing toward a defined goal, adjusting as conditions change.

1. Collect and process data to understand the problem

The workflow begins by gathering data from across systems, whether structured metrics, unstructured text, or real-time signals. Using tools like NLP, LLMs, and live dashboards, the agent builds context around the task. Instead of reacting to a single input, it forms a more complete picture of what’s happening and why it matters.

2. Make decisions using predefined goals and past experiences

With the problem defined, the agent evaluates potential paths based on current inputs, prior outcomes, and constraints. This kind of decision-making resembles a rational AI agent—one that takes actions expected to move it closer to its goal. Unlike static automations, the workflow can weigh options and adjust in real time.

3. Perform tasks and solve problems

Once a direction is chosen, the agent executes. That might mean sending an alert, generating a report, updating a system, or prompting another workflow. If conditions shift during execution, like new data or a failed dependency, the workflow can reroute without starting over.

4. Communicate and collaborate with humans and other agents

Agentic workflows are built to work alongside people, not replace them. Agents might ask for confirmation, escalate an issue, or surface a recommendation for review. They can also collaborate with other agents to split tasks or carry data from one step to the next, streamlining more complex processes.

5. Finalize, reiterate, and learn

After execution, the workflow doesn’t just stop. It captures outcomes, incorporates feedback (implicit or direct), and uses that information to improve future actions. Over time, the agentic workflow creates a system that not only adapts but continues to improve with experience.

These patterns are becoming more common as AI tools take on broader roles in daily work. As McKinsey’s research on agentic AI shows, the most effective systems aren’t just automated; they’re iterative, collaborative, and outcome-driven.

Benefits of agentic workflows

Agentic workflows help teams spend less time managing tasks and more time making progress. By understanding context, making decisions, and learning from outcomes, they reduce friction across processes and give teams space to focus on higher-value work. Below are some benefits they offer:

Handle more without adding headcount

Agentic workflows scale as demand grows, managing parallel tasks and adjusting in real time without requiring more hands on deck.

Keep work moving with fewer delays

By reacting to new data as it comes in, workflows reduce rework, avoid bottlenecks, and minimize constant status checks.

Enhance collaboration between people and systems

Agents can prep data, suggest actions, or ask for input, so teams spend less time setting up and more time moving forward.

Enable consistent decisions

With logic and real-time context built in, workflows help teams act quickly and stay aligned, especially in high-volume areas like AI data analytics.

Improve customer experience automatically

Agents can respond to issues, personalize outreach, and trigger updates in real time, often before a person has to step in.

Reduce operational costs over time 

By cutting down on repetitive tasks, tool switching, and manual oversight, agentic workflows drive long-term efficiency.

Agentic workflow use cases

Agentic workflows can support nearly any team that manages repeatable decisions, shifting priorities, or high volumes of data. Whether it’s automating routine tasks or making real-time adjustments based on performance, these workflows help people work with more focus and flexibility. Here are a few examples of where agentic workflows are already making an impact:

Marketing

An agentic workflow can analyze campaign performance, adjust budget allocation, and recommend next steps, automatically moving underperforming spend to higher-performing channels based on real-time data.

Customer service

Agents can triage support tickets, summarize issues, route them to the right person, and even draft suggested responses—reducing resolution time while keeping human reps focused on more complex cases.

Finance

Teams use agentic workflows to reconcile transactions, detect anomalies, and generate financial summaries, giving analysts more time to interpret the data and less time spent tracking it down.

Retail and ecommerce

Workflows can adjust pricing, personalize product recommendations, or flag fulfillment delays before they impact customers. These actions are driven by both recent activity and broader trends.

Manufacturing

Agents monitor production data and proactively recommend maintenance or rerouting before a disruption occurs, reducing downtime and manual intervention.

Healthcare

Agentic workflows can help triage incoming patient data, suggest follow-up actions, or flag high-risk cases for review. These systems support care teams by filtering noise and prioritizing attention.

These examples reflect a broader shift toward AI business analytics, where workflows don’t just display data; they act on it. And because agentic workflows are modular, they can scale from single use cases to entire processes over time.

Challenges in implementing agentic workflows

While agentic workflows can deliver major gains, building them into existing systems takes thoughtful planning. It’s not just about adopting new tools—it’s about changing how decisions are made, how people interact with systems, and how data is used day to day. Here are a few challenges teams may face when getting started.

Technical complexity

Designing workflows that make decisions, connect across platforms, and learn from feedback requires more than simple automation. Teams should have access to clean, unified data and the right level of AI support to make the system useful—not overwhelming.

Privacy and security concerns

Giving agents access to sensitive or customer-facing systems introduces risk. Teams should keep workflows operating within defined boundaries and comply with company standards. Clear data governance is essential, especially in industries like healthcare, finance, and education.

Transparency and accountability

When agents take action, teams should understand how and why decisions are made. Without visibility, trust breaks down. To maintain trust, AI governance tools help teams monitor workflow behavior, validate decisions, and make adjustments when needed.

Change management

Even when the tech is solid, introducing agentic workflows requires a shift in how people work. Teams need clarity on where automation fits, how roles may evolve, and what human judgment still owns.

Getting ahead of these challenges—especially with strong data practices and well-defined goals—makes the transition smoother and helps agentic workflows deliver long-term value without introducing confusion or risk.

Agentic workflow best practices

Agentic workflows work best when they’re designed with a clear purpose, trusted data, and a strong feedback loop. For teams just getting started or scaling from a pilot, these best practices can help guide the process.

1. Evaluate your readiness

Start with your data. If it’s fragmented, inconsistent, or hard to access, workflows will struggle. Teams should assess whether they have clean, connected, and actionable data to support agentic decisions.

2. Identify workflows with the right level of complexity

Look for use cases that involve repeatable tasks, time-sensitive decisions, or multi-step processes. These are often the best candidates for automation with a degree of autonomy.

3. Choose tools that support iteration

Agentic workflows require more flexibility than static automation. Use platforms that let you refine logic, adjust prompts, and monitor results without rebuilding from scratch.

4. Start small with a pilot

Test with a single workflow, track outcomes, and get team feedback. Pilots help build confidence, reveal gaps, and surface unexpected insights before scaling.

5. Train and upskill your teams

Agentic workflows shift how people work. Make sure teams understand how agents operate, where human oversight is needed, and how to step in when something doesn’t look right.

6. Scale with intention

Don’t automate for automation’s sake. Add new workflows where the impact is clear and the process is well understood.

7. Monitor, measure, and adjust

Review performance regularly, both the decisions agents make and the value they deliver. Feedback loops help workflows improve, but only if you’re paying attention.

Done right, agentic workflows don’t just speed things up; they make work more sustainable. They help teams focus on decisions that matter and remove friction from day-to-day operations.

Ready for an agentic workflow?

Agentic workflows represent more than a technical upgrade; they’re a shift in how teams get work done. By combining context, autonomy, and continuous learning, agentic workflows help people focus on decisions that move them forward. 

Whether you’re exploring your first agentic workflow or ready to scale, Domo provides the tools to make it possible—with the visibility, flexibility, and data foundation to do it right.

Want to see what agentic workflows could look like in your environment? Contact us to start the conversation.