What are agentic workflows? Everything you should know

Traditional automation, built for repeatable tasks in stable environments, can only go so far—modern software delivery has outpaced what static scripts and fixed workflows can reliably handle.

What’s starting to complement this is a more flexible way to run automation. Instead of hardcoded steps, these newer systems work toward a goal.

They decide what to do next, calling the right tools they need, and adjusting on the fly when something breaks. That’s the core idea behind agentic workflows.

That shift is already underway. According to Gartner (as cited by Slack), agentic AI is expected to introduce a goal-driven digital workforce that can plan and act alongside humans.

This post breaks down what agentic workflows are, how they work, and what it takes to implement them in a way that’s reliable, observable, and production-ready.

What are agentic workflows?

Agentic workflows are processes where AI agents take a goal, decide on the steps to get there, use tools to act, and keep adjusting until the job is done—without needing constant human input.

Here, instead of waiting for your question to give answers, the system runs with the task.

As Anna Gutowska and Cole Stryker of IBM put it, “Agentic workflows are AI-driven processes where autonomous AI agents make decisions, take actions and coordinate tasks with minimal human intervention.” 

At the core of agentic workflows is the AI agent itself—a system that combines a language model with tools, memory, and some level of reasoning to act.

Instead of just generating answers, the agent can do things. It can call APIs, query databases, run scripts, check results, rethink its approach, and keep adjusting until it gets an outcome.

In practice, think of a customer whose payment was declined, but their bank still charges their account. A traditional system would match keywords and return a generic response.

In an agentic workflow, AI agents would pull the customer’s details, check the transaction, query the billing system, review the refund policy, and either resolve it or hand it to a human with all the context.

We’ll use this same example to walk through how agentic workflows work and the core patterns that power them.

What makes a workflow agentic?

So what actually makes a workflow agentic? Although the word is often used loosely, not everything that can take action or uses AI fits, and a few things usually stand out. Here are some of the qualities that make a workflow agentic:

1. Makes a plan

The system should be able to look at the goal and figure out the steps before doing anything. It should think more about “What needs to happen to get this done?” instead of “What was I asked?”

2. Reflects and adjusts

After each step, it evaluates the results and adjusts. That loop—plan, execute, evaluate, adjust—is the closest I’ve seen software get to working through a problem rather than just responding to one.

3. Remembers context

And finally, it needs to remember what it already tried. Otherwise, every step is just step one.

How do agentic workflows work?

We already saw this with the payment support example—the customer whose card was declined but was still charged. Let’s walk through what went on under the hood to see how agentic workflow works.

Step 1: Understand the problem

The agent builds context before acting—reading the message, identifying two distinct problems, and determining what it needs before calling a single tool.

Step 2: Plan execution steps

It generates a plan at runtime (not from a fixed script) and decides which systems to query and in what order based on what it just learned.

Step 3: Use tools

It can call the payments API, cross-reference the billing system, or pull the refund policy. Each tool call is selected because the previous result pointed toward it.

Step 4: Adjust based on feedback

If the transaction state comes back ambiguous, it reassesses and tries a different approach rather than escalating immediately.

Exactly what Andrew Ng, founder of DeepLearning.AI, described when an agent’s web search tool failed mid-task—it substituted Wikipedia and finished the job by taking another path to the same outcome instead of failing.

Step 5: Finalize and learn

Once resolved, the agent either closes the ticket or hands it over to a human with the full reasoning trace attached—every system queried, every result observed, and every decision made.

So the support team can pick up midstream instead of digging into the issue from scratch.

The key thing about all five stages is that they form a loop. Agentic workflows don’t terminate at the first output; they refine, retry, and continue until the goal is met or a hard limit—a cap on retries or time—forces it to stop.

Key components of an agentic workflow

We have covered what agentic workflows are and how they work. What makes all of that possible are seven core components working together. Here is what each one does.

1. AI agents

They’re the autonomous system responsible for taking a goal, breaking it into subtasks, and executing it without step-by-step human input. They’re also the orchestrator in the system. They hold intent, manage the action loop, and decide when the task is complete.

2. Large language models

This is the reasoning layer where LLMs interpret ambiguous instructions, select next steps, evaluate outputs, and generate plans. Without them, you’re back to rigid, deterministic scripts. This is where the judgment and context-aware decisions come from.

3. External tools

Tool access is the bridge between generating text about an action and taking the action. It could be access to APIs, databases, code interpreters, web search, file systems, or anything the model can invoke at runtime by emitting a structured function call like tool_use in Anthropic’s API or tool_call in OpenAI’s API.

4. Memory systems

Agentic workflows make use of both short-term and long-term memory. Short-term memory lives in the context window and is scoped to the current session.

Long-term memory, on the other hand, is persisted via vector stores or key-value stores like Redis for fast, direct retrieval. Without memory, every task starts from scratch, and the agent cannot learn, adapt, or compound knowledge from prior executions.

5. Feedback mechanisms

These are human-in-the-loop checkpoints for sensitive actions, agent-based validation for outputs, and guardrails that enforce limits. This keeps execution reliable and controlled.

6. Multi-agent collaboration

Specialized agents handle different roles—planning, execution, validation—working in parallel and sharing context. This scales complexity without chaos.

7. Human oversight

This layer is nonnegotiable. Humans need to define goals, set execution boundaries, approve critical actions, and handle edge cases, focusing their effort where their judgment matters most.

Humans need to define goals, set execution boundaries, approve critical actions, and handle edge cases, focusing their effort where their judgment matters most.

Agentic workflows vs. traditional automation

While agentic workflows are still emerging, their differences from traditional automation are becoming clear. Here are some key distinctions between the two:

1. Trigger

Traditional automation is event-based. For example, a new invoice could trigger a script that runs its steps and stops. Agentic AI works toward a goal, like “process all invoices and flag issues,” adjusting as it goes until the task is done.

2. Flexibility

Traditional automation can break down over something small, like switching a date from DD/MM to MM/DD. It’s just following patterns, so once those change, it fails.

Agentic systems get the context, so those formats all mean the same thing, and it keeps going.

3. Error handling

When traditional automation breaks down, it stops and logs failures, leaving you jumping between timestamps, logs, and error messages, which can be really frustrating.

On the other hand, agentic workflows retry or try different approaches until the task succeeds or it hits its limit, and with proper tracing using tools like LangSmith or OpenTelemetry, you can see what it tried at each step.

Here’s a side-by-side breakdown of how they compare across key areas.

 

Benefits of agentic workflows

Below are the practical benefits you will get from using an agentic workflow.

1. Flexibility and adaptability

Having dealt with traditional systems breaking over the smallest of changes like data formats, I appreciate this: agentic systems look at the context, adjust, and try another way instead of failing.

2. Improved performance on complex tasks

Agents break multi-step problems into smaller actions, use the right tools at each step, and get more reliable results than a single LLM call.

3. Self-correction and learning

Agents don’t just fail and stop. They check outputs, detect issues, adjust, and move forward. With memory, they also get better over time.

4. Operational efficiency and scale

The system takes on more of the work as you scale, instead of creating more to manage.

5. Better user experience

With an agentic workflow, responses come with context and resolution, shifting human involvement to where their judgment matters more.

Challenges and limitations

Agentic workflows are genuinely powerful, no doubt. But they’re also not something you just throw at every problem. In the wrong setting or with the wrong constraints, they will likely make things worse.

Here are a few limitations worth keeping in mind:

For a task that runs the same way every time, a script will usually outperform an agent.

1. Unnecessary complexity for deterministic tasks

For a task that runs the same way every time, a script will usually outperform an agent. It’s faster and cheaper. Introducing an agent adds LLM inference costs, nondeterministic behavior, and orchestration overhead to a problem that did not need any of that.

2. Difficult legacy system integration

Most organizations with legacy systems struggle to connect their agents. They often need custom API wrappers, middleware, and infrastructure to make a connection possible, which isn’t easy to maintain.

3. Increased autonomy introduces nondeterministic behavior

In an agentic system, more steps usually mean more places for things to go wrong. The model could drift or misread context and then make a bad call. This is solvable, though, by building in guardrails and human checkpoints from day one.

4. Dependence on prompt quality

An agentic workflow is only as good as the prompts shaping its behavior. A poorly structured prompt produces bad outputs, and it could compound errors across every step of a multi-step workflow.

Prompt engineering in agentic systems is a core infrastructure that requires the same rigor as any other part of the system design; don’t treat it as an optional configuration.

5. Data readiness and quality

Data quality really matters. If your data is scattered or unstructured, your agent will keep making bad calls. I’ve seen this break workflows early. Before deploying at scale, make sure your data is usable.

6. Ethical and oversight considerations

While autonomy is useful, without control, it becomes a risk. So anything involving finance, medical data, legal exposure, or sensitive personal information needs human oversight, regardless of how capable the agent is.

Anthropic’s research found that when AI agents faced threats to their goals, models from every major provider resorted to blackmail and data leaks, and it wasn’t an accident but deliberate reasoning. So, build in oversight from day one.

How to implement an agentic workflow

To build a reliable, production-ready agentic workflow, you need more than just a working prototype. You need structure and guardrails. These steps will help you get there.

Step 1: Define the goal

First, does your goal even need an agent? Be clear. If a simple script can handle it, use that. Agents make more sense when tasks are complex and need multi-step reasoning.

Step 2: Map the workflow

Before coding, sketch your workflow. Where does planning happen, what tools are used, and where do humans step in? You need to be able to map out all these.

You won’t exactly figure everything out upfront; that’s normal, but mapping most of it early helps. Keep it simple: one agent, one clear role.

Step 3: Pick a framework

Choose an orchestration framework that matches your team’s technical skills and your stack. LangGraph and CrewAI are popular choices you can go for. You should not be building all the underlying infrastructure from scratch.

Step 4: Choose your models

Not every step needs the most capable model. Use larger models for reasoning-heavy steps. Use smaller, faster models for classification and routing.

Also, match your model capability to the task complexity at each stage. With this, you can control cost and latency without sacrificing output quality.

Step 5: Integrate your tools

Next, connect your agent to the external systems it needs: APIs, databases, execution environments, and others. Then, define clear input and output schemas for every tool.

Step 6: Set permissions and guardrails

Decide what the agent can do freely and what requires human approval before execution. Apply zero-trust principles. These guardrails will make it safe enough to scale.

Step 7: Test in a controlled environment

Run the workflow against real-world scenarios before deploying. Test edge cases, simulate failures, and see how the system behaves under pressure. Production is not where you discover missing safeguards.

Step 8: Monitor, log, and refine

Log every tool call and its output. Review outcomes regularly. Then, use that data to improve prompts, set guardrails, and close gaps in your design.

Agentic workflows bring more flexibility into automation, so complex, multi-step tasks can run more efficiently without needing as much human involvement all the time.

Agentic workflow use cases

Agentic workflows bring more flexibility into automation, so complex, multi-step tasks can run more efficiently without needing as much human involvement all the time.

Here are some scenarios where agentic workflows can deliver more value for your team:

Research assistants

In research, it can serve as a partner who can retrieve documents from multiple sources, combine the insights, identify contradictions, weigh evidence, and provide conclusions.

Agentic coding assistants

With agentic coding assistants, you can plan your software project, generate code for it, execute it, debug failures, and iterate until the implementation meets your specification.

Given the right permissions and MCP integration, the AI agents can raise pull requests and make commits while you just review and approve.

IT support

In IT support, agentic systems replace rigid decision trees with a more flexible way of handling issues. Instead of following a fixed script, an AI agent looks at the actual error, checks system logs, and works through possible causes.

With the right permissions, it can also test different approaches and adjust based on what it finds, so each issue is handled based on what’s really happening, not a predefined script or path.

Customer service

A customer service agent can retrieve a full case history, check internal docs and past cases, and generate resolution recommendations immediately when an issue drops on your customer service portal, like Zendesk, before a staff member ever opens the ticket.

As Daniel O’Sullivan, senior director analyst at Gartner, put it, “Unlike traditional GenAI tools that simply assist users with information, agentic AI will proactively resolve service requests on behalf of customers.”

This means faster responses and fewer back-and-forths, so humans focus on edge cases and exceptions.

Cybersecurity monitoring

In cybersecurity, speed is everything. AI agents can learn your organization’s network activity, so when something unusual shows up, they flag it, correlate signals, and escalate early.

With finance advisory copilots, you can track spending patterns in real time, identify unused budget, and have the agent recommend where to allocate money or invest.

Finance advisory

With finance advisory copilots, you can track spending patterns in real time, identify unused budget, and have the agent recommend where to allocate money or invest.

The range of agentic workflow use cases is pretty wide. But one area where they really stand out is software testing and delivery, where slow feedback loops and missed defects can directly impact the business.

So, how do agentic workflows impact software testing? Let’s look into that next.

Agentic workflows in software testing

Software testing is one of those areas where agentic workflows flourish. Test suites keep growing, code changes all the time, and when a defect reaches production, the bill comes in three currencies: revenue, reputation, and engineering time to fix what should have been caught earlier.

Let’s see how agentic workflows change the way this is handled.

Test generation

When a developer pushes a commit, the agent reads the diff, tries to understand what changed, and generates tests around that. Not random tests, but ones that target the new logic and past failures.

Test prioritization and selection

Running the full test suite on every commit is slow and unscalable. With agentic workflows, one or more AI agents look at what changed, figure out the blast radius, identify which existing tests are relevant, and run those first.

Risky changes get full coverage, lighter changes get just enough validation, all based on actual priorities, not some static setup.

Coverage gap analysis

Line coverage only shows which lines ran, not whether important logic was tested. Agentic workflows go deeper by analyzing the code, spotting untested paths or edge cases, and generating targeted tests to cover those gaps and improve test quality.

Defect triage

When a test fails, instead of the usual stack trace archaeology we see in traditional systems, the agents work out what broke, trace it to the code change that caused it, and either fix it or hand your engineers a diagnosed problem with full context attached.

How Tricentis operationalizes agentic workflows

The challenge with agentic workflows in testing is making them usable in real testing pipelines without things getting complex. That’s the gap Tricentis focuses on.

Instead of leaving teams to wire agents together from scratch, Tricentis gives you a structured way to run them in production. It turns agentic workflows from something experimental into something you can operate day to day.

The AI workspace acts as the control layer. You define workflows, set rules, and decide where human approvals sit. So even though agents are running tasks, you still have visibility and control.

Then tools like Tricentis Tosca, Tricentis qTest, and SeaLights plug directly into that system. Tests get generated, updated, prioritized, and analyzed for coverage gaps based on real-time system changes.

What this really does is remove the heavy lifting. You’re not guessing how agents behave or stitching tools together yourself. Everything runs as a connected system, with structure, observability, and control built in.

So instead of agentic workflows staying as a concept, they become something you can plug into your pipelines and rely on.

Use case: How a global gaming company cut production defects by 90% with smarter testing

A leading global gaming company serving millions of users was losing release confidence due to growing defects and fragmented testing. Here’s how they fixed it.

Problem

The company’s QA team was shipping two to three production defects every week, with no standardized regression suite and test progress tracked across disconnected spreadsheets.

Solution

They implemented Tricentis qTest for centralized test management and Tricentis SeaLights for test gap intelligence and analytics, which automatically block untested code changes from reaching production and align their test coverage to every code change.

Outcome

The results spoke for themselves. Here’s what changed:

• Production incidents dropped by 90%.
• Release cycles shortened from eight weeks to three.
• The team achieved 100% coverage across all code changes.

See how Tricentis makes this possible. Explore the AI workspace demo.

Unlike traditional automation that breaks on change, agentic systems plan, reason, adapt, and recover, handling the kind of complex tasks that once required constant human involvement.

Conclusion

Agentic workflows represent a genuine shift in how work gets done. Unlike traditional automation that breaks on change, agentic systems plan, reason, adapt, and recover, handling the kind of complex tasks that once required constant human involvement.

The organizations seeing real returns are the ones building the right structure around it. Clear goals, the right tools, human oversight where needed, and guardrails from the start.

Software testing is among the highest-leverage places to start. From generating targeted tests to training failures with full context attached, the impact shows up quickly.

Tricentis (an agentic testing platform) helps make this practical by giving teams the infrastructure to move from experiments to real production workflows. If you are ready to apply agentic workflows in testing, explore Tricentis AI-powered solutions.

This post was written by Inimfon Willie. Inimfon is a computer scientist with skills in JavaScript, Node.js, Dart, Flutter, and Go Language. He is very interested in writing technical documents, especially those centered on general computer science concepts, Flutter, and backend technologies, where he can use his strong communication skills and ability to explain complex technical ideas in an understandable and concise manner.