The rapid evolution of generative AI has meant organisations are already moving from experimetation to implementation in their products and workflows. At the heart of this transition are AI agents—autonomous software systems capable of reasoning, planning, and acting on behalf of users and organisations. They enhance the capabilities of large language models (LLMs) enabling them to not just generate text but also take actions in the real world.
For senior product leaders, engineering executives, and digital strategists, the emergence of AI agents signals more than a technical milestone. It marks the beginning of a new operational model—one where software systems can self-organise, collaborate, and optimise how work gets done. This article outlines what AI agents are, how they work, where they create value, and what leaders should do now to prepare.
AI agents are not new in theory, but recent advances have made them practical, scalable, and strategically relevant. In essence, an AI agent is a software entity that can autonomously pursue a goal. It does this by interpreting tasks, making decisions, coordinating resources, and executing actions.
What has changed is the surrounding ecosystem. Advances in generative AI—particularly in natural language processing—combined with improvements in memory and orchestration frameworks have transformed simple models into sophisticated agents. These agents can now deconstruct ambiguous tasks, collaborate with other agents, and operate across a wide range of tools and environments.
The strategic shift is clear: we are moving from outputs (e.g. generating a document) to outcomes (e.g. coordinating a workflow across tools, teams, and timelines). That distinction is critical for business adoption.
Not all agents are created equal. They vary significantly in design, complexity, and purpose. Understanding the taxonomy helps clarify where to apply them.
Copilot Agents: These agents enhance individual productivity. They assist with writing, coding, research, and decision support. Examples include Microsoft 365 Copilot and OpenAI’s ChatGPT.
Workflow Orchestrators: These agents execute structured processes, often spanning multiple systems. They serve as digital process managers—for instance, automating customer onboarding or reconciling financial transactions.
Domain-Specific Agents: Built for specialised functions, these agents are trained to work in specific domains such as software engineering or customer service. Unlike copilots, they’re designed from the ground up with that context in mind.
Most organisations will deploy a mix. Copilots may offer a low-friction entry point, while more ambitious agent strategies will evolve alongside architectural and cultural change.
To appreciate the potential of AI agents, it helps to understand how they operate.
Task Assignment: A user or system assigns a task—typically expressed in natural language or triggered by an event.
Planning and Delegation: A manager agent breaks the task into subtasks and delegates them to specialist subagents. These subagents may draw on prior experience, access external APIs, or query knowledge systems.
Execution and Coordination: Subagents execute their work—writing code, fetching data, completing forms—often in parallel. They coordinate via shared memory, messaging, or central orchestration logic.
Iteration and Feedback: The system may refine its work based on user feedback or internal critique mechanisms. For example, a critic agent might review a draft before submission.
Action Delivery: Once the task is complete, the system delivers the result—whether it’s a decision, a document, or a downstream trigger.
Agents can operate across human-facing tools (like a web browser or spreadsheet) and machine-facing interfaces (like APIs or cloud functions), making them uniquely suited for heterogeneous environments.
It’s important to separate the capabilities of LLMs from the systems that use them. LLMs are generative models that produce content, analyse data, or interpret queries. They provide the language interface.
AI agents, by contrast, use LLMs (and other models) as components. An agent may use an LLM to interpret a task or generate an email, but it also incorporates logic, memory, decision rules, and execution capabilities.
A helpful analogy is a self-driving car. One agent may use an LLM to understand a passenger’s request (“Take me to the airport”), while others handle real-time navigation, obstacle detection, or route optimisation using entirely different models. The agent system is a coordinated network of capabilities.
AI agents deliver value in two primary ways: by improving productivity and by enabling fundamentally new ways of working.
Rather than simply automating discrete tasks, agents can help re-architect workflows, reduce dependencies, and enable faster decision-making. This is particularly relevant in environments where processes are fragmented or involve multiple human and software handoffs.
Morgan Stanley has rolled out a generative AI assistant to 16,000 financial advisors. The agent surfaces relevant content from over 100,000 internal documents, significantly improving response times and quality of client service.
Volkswagen uses AI agents to streamline software engineering tasks like code documentation and review. This reduces technical debt and accelerates product development across teams.
These examples reflect a broader trend: AI agents are beginning to act not just as tools, but as collaborators embedded within business processes.
AI agents are not simply better automation tools. They represent a new class of system entirely that can:
Handle Ambiguity: Traditional workflows break down in edge cases or unexpected inputs. AI agents, trained on unstructured data, can adapt to novel scenarios and reason through exceptions.
Enable Natural Interfaces: Instead of writing scripts or defining flows in BPM tools, users can now instruct agents using plain language. This opens up complex automation to non-technical stakeholders.
Operate Across Boundaries: Because agents are built on foundation models, they can navigate diverse software systems without brittle integration. They can read screens, parse documents, call APIs, and interpret human inputs fluidly.
Despite clear promise, agent adoption is not without friction.
Trust and Explainability: Users need to understand how and why agents make decisions. Some companies are layering in critic agents to catch hallucinations or flag uncertain outputs before surfacing them to customers.
Change Management: Successful adoption isn’t just about new tools. Organisations need to rewire workflows, train teams, and incentivise new behaviours.
Security and Governance: Data privacy, regulatory compliance, and operational control remain central concerns. Any agent implementation must include robust oversight mechanisms.
The companies that succeed will be those that design with trust, structure, and transparency in mind from the outset.
Leaders evaluating AI agent strategies should start with a few clear principles.
Interrogate Tech Proposals: Scrutinise large transformation programmes—ask whether agents can reduce cost, shorten timelines, or improve adaptability.
Prioritise High-Leverage Problems: Focus on complex, expensive, and long-running issues—not just easy wins. Agents are especially valuable in areas with high fragmentation or technical debt.
Align Talent and Models: Upskill teams to work with agents, not just use them. Structure small, iterative teams that can build, test, and refine agent systems with clear feedback loops.
The right investments today will compound as agent ecosystems mature.
AI agents are becoming the new interface between human intent and machine execution. Their ability to reason, coordinate, and act makes them qualitatively different from previous waves of automation.
For product & tech leaders, the question is no longer whether to use agents at all, it’s identifying where to use them and how to implement them effectively. The organisations that build agent-oriented capabilities may improve efficiency today, but over time they will change how work happens.