What is agentic AI and what does it mean for the enterprise?

What is an agent?

At its core, an agent is an AI system designed to act autonomously towards completing a goal. Unlike traditional software that follows a rigid set of instructions, an agent can make decisions, adapt to new information and plan next steps based on a changing environment.

Two key features make agents powerful:

• Memory: Agents can retain context across interactions. They don’t just react to isolated inputs, they build an evolving understanding over time.

• Tool Use: Agents can leverage external tools, APIs, databases and systems to extend their functionality beyond the base level of knowledge available in an LLM.

Emerging standards like the Model Context Protocol (MCP) are also making it easier for agents to communicate and interact with tools safely and reliably. Protocols like these help drive interoperability, enabling agents to integrate more seamlessly into diverse enterprise ecosystems.

Single agent systems

Today, many practical agentic AI deployments focus on single agent systems. These systems are designed around one AI agent managing a defined set of objectives. 

Here’s a simplified overview:

1. Goal Definition: The agent is given a clear objective like “reduce IT infrastructure downtime” or “optimise customer onboarding workflows.”

2. Environment Perception: The agent gathers data from its environment, such as system telemetry, user activity or external events.

3. Memory Updates: It updates its internal knowledge base based on outcomes, feedback and ongoing interactions.

4. Tool Interaction: The agent actively uses APIs, control systems, SaaS platforms or internal apps to gather information and perform tasks.

5. Decision-Making: It plans and executes actions autonomously, adjusting as new information becomes available.

Imagine an agent tasked with managing enterprise cloud costs. Instead of merely reporting usage data, the agent could proactively optimise resource allocations, decommission unused services and recommend cost-saving configurations - all with minimal human intervention.

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Multi agent systems

While single-agent systems are common today, multi-agent systems - where groups of agents collaborate, delegate or even compete - are becoming much more widespread. These systems can handle even more complex challenges but require higher levels of orchestration and governance.

Single-agent systems are best suited for problems where a centralised perspective and control are sufficient, such as tasks with limited scope or where coordination is not required. In contrast, multi-agent systems are more appropriate for complex, distributed environments where agents must collaborate or compete to achieve their goal. This allows for the creation of agents with expertise in a specific domain and makes it much easier to test agents in isolation from the wider system.

Real-world examples

Here’s how agentic AI is already reshaping enterprise environments:

• IT Operations Management: Agents detect anomalies, predict outages and automatically remediate incidents before users are impacted.

• Customer Service Automation: Intelligent agents handle complex support workflows, integrating across CRM, knowledge bases and escalation paths without constant human oversight.

• Business Process Optimisation: Agents analyse operational workflows, suggest improvements and even pilot changes dynamically based on real-time business KPIs.

Why this matters

Senior business leaders are at a pivotal point - the systems you oversee are becoming increasingly difficult to manage with interdependencies and rapid changes that outpace traditional methods. Agentic AI offers a way to retain control while scaling agility and resilience.

The future isn’t just about smarter tools. It’s about systems that can understand, adapt and act independently - agents are at the heart of that transformation.

In subsequent articles, we will dive into the details of agentic systems and multi-agent architectures, examining the advantages and disadvantages of each, and how to deal with cost and security concerns.