Build Agentic AI to Deliver ROI Without ‘Bad AI’ Surprises

Agentic AI is having its moment, and for good reason. Instead of serving as a model that answers a question, an AI agent can actually complete a task. It has the ability to grab the right data, make an informed decision, and take action—such as updating a system, notifying a stakeholder, or answering a customer’s question—and keep the workflow going until the end goal is complete.

This type of AI agent is poised to play an increasingly greater role in businesses. Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% in 2025.

But there’s also a flip side. Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls, according to Gartner.

This challenges business leaders to understand what separates a compelling proof of value from production-grade, ROI-driven, trusted agentic AI. The difference is not just the model. It’s the workflow foundation, especially the data foundation, and whether organizations can observe what’s happening with their data so bad or drifting inputs don’t fuel bad AI decisions.

Understand What Entails an Agentic Workflow

A helpful way to think about agentic workflows is to break them down into these processes:

• The agent gathers information such as data, documents, tickets, events, and KPIs.
• It plans steps, uses tools, and decides what action to take next.
• It executes actions in systems, such as sending an email, updating a record, or triggering a job.
• The agent uses feedback to continuously improve future decisions.

Unlike traditional automation, which is often rigid, agentic workflows are adaptive, meaning they can respond when a situation changes. That flexibility is why they’re so powerful, but also why the risk of using them increases quickly when the agent is operating on incomplete, stale, or poorly governed data.

7 Steps to Build Agentic AI Workflows

Organizations can create reliable and trusted agentic AI workflows by taking these steps:

1. Define the end goal. Start with a workflow where success is measurable and manageable, such as improving revenue cycle performance, hitting on-time delivery targets, or speeding up month-end analysis reports. Then translate that goal into clear rules, like what inputs the agent can use, what systems it’s allowed and not allowed to touch, and what “good” looks like, such as accuracy thresholds.
2. Set guardrails before enabling AI autonomy. Next, implement guardrails by tiering actions: what the agent can do automatically, what it can recommend but requires human approval, and what it must never do without explicit human interaction. Many AI projects stall or fail because teams deploy agents with no clear boundaries, unclear ownership, and no operational definition of “safe.” Without guardrails, even small data errors and overconfident outputs can have downstream consequences.
3. Turn systems into auditable steps. In production, agentic systems work best when they’re viewed as small, single-responsibility steps. These steps can include retrieving, validating, classifying, deciding on, and acting on data. This makes AI agent behavior easier to test, monitor, and govern.
4. Ensure data has trusted context. AI agents need more than rows and columns of data. They need context such as:

• • Business definitions. What counts as an active customer?
  • Relationships. How does Product A map to Service Line B?
  • Policies. What data is restricted, and what actions require approval?
  • Lineage. Where did this metric come from?

Having data context is the difference between an agent that sounds confident, even if an answer is wrong, and an agent that’s actually grounded in the current business reality.

5. Make trust measurable and continuous. If an AI agent is making decisions, organizations need real-time visibility into how data behaves as it flows into and through AI systems. This is where data observability becomes critical. It allows data teams to catch drift, anomalies, and breakages before they become customer-facing or revenue-impacting errors.
6. Ensure data reliability with contracts. One of the most practical ways to scale agentic workflows is to treat key datasets like products, with clear expectations. Data contracts support this approach by defining expected schema, quality thresholds, update frequency, ownership, and usage. That way, an AI agent isn’t guessing what “good data” looks like. It’s consuming a governed data product backed by enforceable guarantees.
7. Implement monitoring, incident response, and governance. For an agent to act, it needs the operational muscle that’s applied to any production system. This includes having alerts that detect and resolve data quality issues, ensuring clear audit trails for visibility, and implementing access controls for approvals. Organizations should also have a plan to identify and correct any problems that could arise.

A Simple 5-Step Roadmap to Get Started

Organizations that want a practical roadmap to build agentic AI workflows can start with these steps:

1. Pick one workflow with measurable value and clear boundaries.
2. Map the decisions the agent will make, and the data needed for each decision.
3. Standardize context with definitions, lineage, and policies so the agent isn’t improvising or hallucinating.
4. Enable observability across freshness, volume, schema, distribution, and lineage.
5. Provide guardrails with human-in-the-loop processes, then expand autonomy as trust becomes measurable.

Don’t Just Build Agentic AI. Build AI You Can Trust.

Agentic AI workflows are not “set it and forget it” automations. They’re living systems, fed by data that can change, break, and drift. That’s why Actian’s message is so important for this moment: don’t just build agentic AI. Build it on data that teams can discover, trust, and activate, and then continuously prove that trust with data observability.

This is how organizations prevent “bad AI” from becoming a reputational, regulatory, or financial issue. See how Actian Data Observability can proactively identify data quality issues, prevent them, and support agentic AI.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Organizations trust Actian data management and data intelligence solutions to streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data and AI division of HCLSoftware, at actian.com.