Agentic AI is capturing headlines, promising intelligent agents that can autonomously take action, learn, and optimize customer experience (CX). But for agentic AI to deliver on its potential, it requires more than simply making data accessible through APIs. Many customer engagement technology vendors claim to be in the agentic AI game, but in reality they’re doing little more than making software components callable by an external agent – whether or not that external agent autonomously completes a task.
True agentic AI requires clean, accurate, and timely data that is fit-for-purpose for agent use, making data readiness a requirement to maximize the value of agentic AI across the enterprise. More importantly, agentic AI requires a platform that does more than pass data to external agents; it actively participates in the agentic process itself.
Agentic AI Is More Than API Access
Simply exposing data via APIs to allow external agents to call data for their workflows is certainly important as a first step to agentic AI, which is why a data readiness platform – in addition to making data right and fit-for-purpose – should also make its software components callable by agents in ways that optimize agent knowledge, actions and workflows for success.
To truly support agentic AI a platform must empower agents that act autonomously using high-quality data and the proper tools to carry out actions in any given context. The better and faster the callable components are, the better agents can optimize actions to reach their goals. This is a key difference between simply handing data off vs. becoming active participants in the agentic process.
Human-Centered vs. Data-Centered Agentic AI
Agentic AI can take many forms. Some agents are human-centered, acting on natural language prompts like “build me a segment” or “show me a journey visualization.” Redpoint enables these interactions through prompt-based workflows that allow human users to request insights, actions, or visualizations – delivering immediate, explainable outputs.
But the future of agentic AI also includes data-centered agents that participate directly in workflows. These agents can:
- Perform identity resolution autonomously when triggered by external systems.
- Cleanse, match, and standardize data in response to requests from other agents.
- Monitor campaigns and trigger actions based on results.
- Evaluate when customer data and contextual signals are ready to launch a campaign or nurture workflow.
Unlike traditional triggered actions that require rigid rule-setting, agents have the autonomy to determine when data is ready, when criteria are met, and when to act. They blend intelligence with action, reducing manual intervention while ensuring high-quality outcomes.
Building Toward True Agentic AI
There is a maturity curve to agentic AI that consists of three distinct parts. The first step is to allow agents to call and use high-quality data. Again, this is what some vendors are now claiming as full-on agentic AI vs. a stepping stone to the next step, which is data made actionable for agents. This means providing active metadata, contextual signals, and aggregations that make data understandable and usable by agents. The third step crosses the threshold into what can truly be called agentic AI: Developing intelligent agents that can act on human prompts or agent requests, making decisions based on context, and taking actions autonomously across the customer engagement journey.
Examples of agentic AI supported by a robust data readiness platform might include:
- A segmentation agent that recommends and creates dynamic segments that may be used in orchestrating customer journeys.
- A data observability agent that monitors data health, signals when data is ready for campaign activation, and autonomously executes the process.
- An ID resolution agent that ensures customer identities are updated in real time or households are validated before personalized interactions occur.
- A campaign readiness agent that autonomously checks for campaign prerequisites, ensuring content, permissions, and triggers align before launch.
- An integration agent that interacts with third-party systems like Braze, Adobe, or Salesforce to exchange data, optimizing timing and content to reduce campaign costs and increase reach and effectiveness.
Preparing for an Agentic AI Future
Agentic AI, when paired with AI-ready data, creates a new level of customer engagement – one that is responsive, intelligent, and optimized in real time. But it can only succeed when the agents have access to clean, trusted, and real-time data, and when the platform is designed to support autonomous action, not just passive data delivery.
At Redpoint, we see agentic AI as a natural evolution of our data readiness mission: enabling businesses to engage customers with intelligence and precision while maintaining control, transparency, and trust. Redpoint’s architecture supports the transition from AI-ready data to embedded AI and now to agentic AI.
As we continue to develop our agentic AI capabilities, we will share deeper insights into how Redpoint can help your organization harness autonomous, intelligent agents to optimize customer engagement at scale.
