If you work anywhere near AI or automation, you’ll have noticed the huge growth in the use of the term agentic AI.

It’s sometimes described as the next evolution of conversational AI, where systems won’t just respond to requests, but will be able to act autonomously, plan steps, make decisions, and execute tasks on a user’s behalf.

It’s a powerful concept. But it’s also being widely misunderstood.

In this article, we’ll explain what agentic conversational AI actually is, how it differs from older chatbots and virtual assistants, and why most organisations should be cautious before rushing to adopt it.

What Do We Mean by “Agentic” Conversational AI?

At a high level, agentic AI systems are designed to:

  • Interpret a goal rather than a single intent
  • Break that goal into multiple steps or tasks
  • Decide which actions to take
  • Interact with other systems, tools or APIs
  • Adjust behaviour based on outcomes

In a conversational context, this means moving beyond “Here’s the answer to your question” and instead towards “I understand what you’re trying to achieve, and I’ll take the necessary steps to make it happen.”

Examples often cited include:

  • Booking travel across multiple systems
  • Proactively resolving account issues
  • Orchestrating end-to-end customer journeys without human input

How This Differs from Most Conversational AI Today

Most conversational AI in production today is:

  • Reactive, not autonomous
  • Constrained by defined flows and rules
  • Optimised for containment, deflection and efficiency
  • Carefully governed to protect customer experience and brand risk

Even when powered by large language models, these systems are typically designed to:

  • Answer questions
  • Guide users
  • Hand off safely to humans
  • Operate within clear boundaries

Agentic AI, by contrast, introduces:

  • Decision-making authority
  • Multi-step reasoning
  • Tool and system control
  • A higher tolerance for uncertainty

These are meaningful differences, not just technically, but also organisationally.

Why “Agentic” Is Being Over-Hyped

The excitement around agentic conversational AI often skips over some uncomfortable realities:

1. Autonomy Increases Risk

The more freedom a system has to act, the harder it becomes to:

  • Predict behaviour
  • Guarantee compliance
  • Protect brand voice
  • Prevent edge-case failures

For customer-facing environments, these are non-trivial.

2. Governance Is a Genuine Bottleneck

Agentic systems raise difficult questions:

  • Who is accountable for the decisions the AI makes?
  • How are actions audited and explained?
  • What happens when the AI makes a “reasonable” but undesirable choice?

Most organisations don’t yet have clear answers or processes to support these systems.

3. Many Existing Bots Aren’t Even Optimised

In practice, many teams are still struggling with:

  • Poor intent coverage
  • Low containment
  • Weak conversation design
  • Minimal ongoing optimisation

Jumping to agentic AI without fixing these foundations often compounds problems rather than solving them.

Where Agentic Approaches Do Make Sense

This doesn’t mean agentic conversational AI has no place. However, it does mean you want to choose carefully where it goes.

The strongest early use cases tend to be:

  • Internal-facing tools (e.g. employee support, ops automation)
  • Low-risk environments with clear rollback options
  • Well-governed domains with mature data and system integration
  • Narrow, high-value workflows rather than open-ended conversations

In these contexts, autonomy can be a genuine advantage rather than a liability.

The More Important Question: Are You Ready?

Before asking “Should we adopt agentic conversational AI?”, most organisations need to ask:

  • Do we trust our current conversational AI outputs?
  • Do we have clear ownership and governance?
  • Are our data, systems and processes well understood?
  • Can we measure success beyond novelty?

In many cases, the most valuable next step isn’t agentic AI, but it’s getting existing conversational AI to perform properly and getting your other organisational systems set up to enable your conversational interface to undertake meaningful actions.

Final Thought: Capability Before Autonomy

Agentic conversational AI represents a powerful direction of travel, but it’s not a shortcut.

Organisations that see the best results will be those that:

  • Treat conversational AI as a capability rather than a feature
  • Invest in design, optimisation and governance
  • Introduce autonomy deliberately, not by default

In short: the autonomy you give to agents should be earned, not assumed.