AI Agents: Understanding the Next Wave

OK, so there’s a lot written about AI Agents already, and if you’re not already drowning in excitable claims about the end of SaaS (or just SW development as a whole) as we know it, then you’re not living in the same bubble as me. However, there’s still a lot of vagueness about what an AI Agent actually is and does. Adding to the complexity, the landscape is rapidly shifting, not just in agent capabilities but in how they connect to the digital world. So, I thought I’d lay out some basics, updated for the latest trends.

👉 Start here: NVIDIA’s AI Agents Glossary

If you're new to this area, then beyond this article I recommend starting with NVIDIA’s AI Agents Overview. Once you’ve had a look, the categorisation I outline below will help make sense of the key concepts, technologies, integration trends, and practical use cases that are emerging—fast.

1. What Are AI Agents?

AI agents are a step beyond prompt-based Generative AI. They’re systems that perceive, reason, plan, and act—not just once, but across multiple steps, using memory, tools, and real-world context to achieve defined goals.

What makes them different? Traditional GenAI tools generate content in response to a prompt. AI agents, by contrast, maintain state, adapt to new information, use APIs and databases, and can carry out entire workflows autonomously. They can retrieve information, break down tasks, generate plans, execute them, evaluate the results, and even reflect and improve. They're not just systems that talk; they’re systems that do.

Why Is This Important?

AI agents are being widely seen as the next major inflection point in AI adoption for several reasons. They scale cognitive effort—handling tasks that traditionally required human judgment, multi-step logic, or contextual awareness. They enable organisations to automate not just decisions, but also actions.

💡Learn more about MCP: Model Context Protocol Github

And perhaps most significantly, they introduce the possibility of deploying AI as true digital workers operating independently, continuously, and adaptively.

A crucial catalyst accelerating this is the push towards standardised AI integration, particularly the Open Standard called MCP (Model Context Protocol). Think of it like the "USB-C of AI integrations"—a move towards universal connectors that allow agents, tools, data sources, and platforms to plug into each other seamlessly. This interoperability dramatically lowers the barrier to building sophisticated, multi-component AI systems and is a key reason why all major players, from OpenAI and Google to Microsoft and Anthropic (the authors of the MCP standard), are now investing heavily in agent infrastructure and orchestration. Easier integration means agents can leverage a wider ecosystem of capabilities, making them far more powerful and practical for real-world applications.

2. The AI Agent Landscape

To help structure this rapidly evolving field, I’ve organised the key elements into four categories:

A. Core Agent Types & Architectures

This is where we define how agents behave and interact. Some are reactive, responding immediately to inputs. Others are deliberative, capable of reasoning and plotting multi-step plans. Hybrid agents combine both behaviours.

Key advancements include:

• Memory: Allowing agents to personalise interactions and learn from past experiences.

• Tool-Using: Enabling agents to call external APIs, use databases, or trigger processes. The easier it becomes to connect these tools (thanks to integration standards), the more capable agents become.

• Advanced Reasoning: Frameworks like ReAct (Reason+Act) enable iterative reasoning and action, while self-reflection allows agents to critique and improve their own performance.

• Multi-Agent Systems: Agents working collaboratively, sometimes taking on specific roles within a team.

B. Frameworks & Infrastructure

Agents rely on infrastructure to define, run, and coordinate their activities. This is where the impact of standardised integration is most apparent.

• Agent Frameworks: Microsoft's AutoGen and CrewAI enable modular, team-based AI systems. LangChain and its LangGraph component provide graph-based architectures for complex, stateful workflows.
  

  

  AI agent architecture stack — from frameworks to execution workflows.
  Source: Custom diagram created by Attercop.

• The Integration Layer: Emerging standards and protocols aim to simplify how agents connect to tools and data. This "plug-and-play" approach means developers can spend less time building bespoke connectors and more time defining agent logic and workflows. It makes the underlying infrastructure more robust and scalable.

• Workflow Orchestration: Concepts like directed acyclic graphs (DAGs) remain fundamental for modelling dependencies and execution flows in a controlled way, but standardised interfaces make integrating each step within the graph much simpler.

C. Memory, Planning & Reasoning

This is the cognitive core. Planning breaks goals into steps, reasoning decides the actions, and memory provides context.

• Reasoning Techniques: Chain of Thought and Tree of Thought guide agents through intermediate steps. ReAct and Reflexion enable iteration and learning. Counterfactual reasoning allows agents to explore "what if" scenarios.

• Memory Architectures: Ranging from short-term (dialogue tracking) to long-term (storing facts, strategies). Effective memory is crucial for agents performing long-running tasks or interacting across multiple sessions.

D. Tooling, Evaluation & Metrics

This is the operational layer where improved integration has the most immediate impact.

• Tooling: Equipping agents with tools (calculators, APIs, retrieval models) and the logic to use them. Standardised integration dramatically expands the potential toolkit for any agent, making it easier to leverage existing software and services.

💭 Wondering how agents actually “use tools” like a human might? Click here.

• Orchestration: Coordinating multiple agents or complex workflows.

• Evaluation: New metrics are needed, like task completion rate, tool interaction efficiency, context adherence, and cost per task. LLM-as-a-Judge offers qualitative assessment.

• Structured Output: Ensuring agents produce reliable outputs (e.g., JSON, Pydantic) is critical for enterprise use, especially when integrating agent actions into larger business processes.

3. Common Use Cases for AI Agents

The easier integration becomes, the more sophisticated and interconnected these use cases can be:

Final Thoughts

AI agents are no longer theoretical. They’re practical, composable, and increasingly effective. The drive towards standardised integration is a powerful accelerant, making it easier to build, deploy, and scale agents that interact meaningfully with the complex web of tools and data sources we use every day. This isn't just about automation; it’s about augmentation, orchestration, intelligent delegation, and creating truly interconnected intelligent systems.

This shift makes the development of sophisticated agentic AI more accessible and paves the way for agents to become integral components of future software and business processes.

For insights tailored to your unique challenges, or to explore how AI might unlock new opportunities for your organisation, we invite you to contact Attercop. Let’s discuss your project needs and ideas at [email protected]

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