The Front Lines of​ Agentic Delivery: ​ 6 Hard-Won Lessons​

Agentic automation is moving fast. Tools like UiPath’s Agent Builder and dynamic agentic orchestration make it incredibly easy to go from idea to working agent in days. But the hard part is getting from “it runs” to “it reliably delivers outcomes in production.” Gartner estimate that a 40% of agentic AI projects will be cancelled before they ever deliver value.

These six lessons focus on what you can control today: how you design, test, govern, and roll out agents with your business partners.

Getting AI agents from 0 to 1 is fast. Perfecting it is not. With today’s tools, you can spin up a functioning agent in days. But testing is where the real work begins.

As more effort shifts from development to testing, delivery teams and businesses need to be disciplined about treating testing as a fully collaborative exercise that starts early, not at the end. 

Test sets need to be driven by the business and aligned with specific outcomes, not just technical or performance validations. It is no longer “did the process work and follow the defined path”; but rather “did the agent understand the ask, and how did it perform in the context of what each scenario was expected to achieve.” 

For that to work, business teams need to understand the instructions given to the agent, run validations, catch edge cases, and provide relevant feedback back to drive the desired outcomes. UAT and end user engagement have always been important, but in an agentic world, they sit at the center of delivery. 

Bottom line: agentic development is the sprint, testing is the marathon. If you do not account for both in planning, your agent will not prove business value in production.

Agentic tech is incredibly flexible and highly autonomous. That is its superpower and its risk. 

Traditional IT and RPA teams know that establishing best practices at the outset and designing reusable components create economies of scale. It also ensures teams follow a consistent approach, every build. The same applies to agentic automation. 

Businesses should set clear standards for:

Instead of building massive, complex agents and workflows that differ across the organization, set the standards up front and build to where it adds continuous value. As an example, a simple design rule might help: let agents interpret context and make decisions and let technologies like RPA handle calculations and rules-based logic. This separation of duties creates clarity, stability, and flexibility. It lets businesses build once, reuse often, and keep maintenance in check. 

Bottom line: the strongest automation programs do not sacrifice design standards for speed. Best practices are the backbone for delivering outcomes.

Agentic work should improve your economics, not quietly erode them. 

As vendors lean into GenAI and cloud-based computing, platforms like UiPath have shifted to consumption-based pricing. Under these models, even a “simple” agent can trigger hundreds of LLM calls in seconds. If you ignore that, the cost will catch up to you. 

Thoughtful solution design is how businesses stay ahead of this. An automation program that understands how consumption works for both the platform and the LLMs can save thousands of dollars a month. In practice, that means knowing: 

Taking time to think through solution design up front to limit the number of units consumed allows for a more predictable and realistic business case and avoids sticker shock when the process is in Production.

Bottom line: best practices and solution design act as technical guardrails in AI economics to keep costs in check. 

One of the biggest misconceptions in agentic AI is that agents actively “learn.” While that can be partially true in specifically designed scenarios, most enterprise agents do not learn in the same way traditional machine learning models do. Improvement comes from the underlying models and the prompts, not from the agent itself. Agents are only as good as their prompts, their context, and the version of the model they sit on. When that model updates or new scenarios appear, behavior can change overnight. 

This is why prompt engineering and ongoing fine tuning is not a side skill; it sits at the center of agentic automation. Tools and guidelines can help businesses create strong initial prompts and instruction sets, but tuning, refining, and maintaining those prompts is the real feedback loop. Clear instructions and guardrails are now one of the most important parts of the solution kit. Returning to fine tune based on real usage is how teams improve accuracy, consistency, and alignment with business intent. 

Agents are powerful, but can be unpredictable. They can improvise, hallucinate, and make up information. In testing, we've seen customer IDs pick up extra digits out of nowhere.

For teams running business critical processes in regulated environments, this is unacceptable.

That is why governance is critical. Businesses need controlled agency with human-in-the-loop checkpoints to ensure agentic work is completed accurately and in compliance with policy and regulation.

Governance also pays off through staged rollouts. Start small, measure performance, and scale in waves. Each iteration builds reliability, trust, and a clear link back to business value.

Bottom line: without strong governance and controlled agency, agentic work is difficult to trust, adopt, or scale.

At the end of the day, success in agentic automation is not about the tech. It's about the people.

Adoption lives or dies by trust. That trust comes from transparency, communication, and education—change management is mission critical.​

When teams treat agents like a black box, employees default to skepticism. When employees understand that agents are here to augment, support, and improve their work (not replace them), adoption and advocacy improves.

1. Baking change into the operating model: Define clear roles, decision rights, and “controlled agency” guardrails so human–AI collaboration becomes part of how the business runs, not a final project step.

2. Enabling frontline teams to own the change: Bring stakeholders into co-design, not just UAT, so teams understand why this change is happening, how agents work, how they support business KPIs, and why adopting them is a win for both the business and their role.

3. Making adoption measurable and iterative: Connect change management to continuous improvement metrics and celebrate “assistive wins” where agents and humans succeed together, so adoption becomes something you can see, measure, and systematically improve over time.

Bottom line: Agentic automation only works when people trust it. The biggest gains come when you invest in change management that informs, involves, and empowers your teams so the tech becomes a better way of working, not a threat.

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