AI agents are increasingly being recognised as the next major evolution in artificial intelligence—distinct from chatbots, scripts, and assistants. But what actually gives an agent its intelligence?
In this article, we examine the core capabilities that define effective AI agents, supported by research and insights from leading organisations.
1. Reasoning: Making Informed Decisions
Reasoning allows agents to evaluate information, identify patterns, and determine the best course of action based on goals. It’s one of the foundational traits of agentic behaviour. Google Cloud highlights reasoning as a primary capability of AI agents, enabling them to analyse data, make inferences, and solve problems contextually rather than reactively. Academic frameworks like ReAct (Reason + Act) also demonstrate how large language models can simulate reasoning chains by interleaving thought steps with actions, making them well-suited for complex decision-making.2. Acting: Taking Meaningful Steps Toward a Goal
An agent is not merely a system that outputs responses—it must also take concrete actions. These may involve querying a database, updating a system, calling an external tool, or triggering workflows. The ability to act is essential to transition from passive assistants to agents capable of driving business operations. Meta’s Toolformer paper demonstrated that language models can teach themselves how to use external APIs to take useful actions in a task. Enterprise tools like cognipeer build on this by enabling agents to interact with real-world systems—CRMs, APIs, or cloud databases—based on reasoning and learned behaviours.3. Observing: Understanding Input and Environment
Agents must be able to observe and interpret their environment, whether through natural language input, sensor data, or structured information. Observation is one of the five cognitive functions IBM outlines as critical to AI agent design, particularly in dynamic or uncertain environments. Effective observation allows agents to stay grounded in reality—responding not just to what’s said, but also to what’s changed.4. Planning: Structuring the Path to the Goal
Planning involves setting intermediate steps and making decisions over time to achieve a larger objective. This is particularly important in goal-directed behaviour, where tasks must be completed in sequence or according to a logic tree. Research on Hierarchical Planning Agents and the popularisation of tools like LangChain show that agents can coordinate multiple actions toward a structured outcome.5. Collaboration: Interacting with Humans or Other Agents
Modern agents are designed to work in teams—either with human users or with other agents in distributed systems. Multi-agent environments allow for task sharing, coordination, and more robust workflows. This concept is also highlighted in academic work on multi-agent systems, where each agent contributes a role in achieving a shared or competing goal.6. Memory: Retaining and Using Context Effectively
Memory enables agents to maintain context across interactions, recall past events, and improve over time. This is key to building coherent experiences. Google Cloud outlines multiple memory types in their AI agent architecture:- Short-term for real-time interaction
- Long-term for historical knowledge
- Episodic for conversation threads
- Consensus for shared memory across agents