AI Agents: Core Concepts Explained

The term 'AI agent' has become overloaded in recent years. At its core, what distinguishes an agent from a standard language model application is the execution loop: an agent repeatedly observes its environment, decides what to do next, acts on that decision, and then observes the results of its action. This observe-decide-act cycle continues until the agent achieves its goal or determines it cannot proceed.

In practice, most production AI agents are built on top of large language models but extend them significantly. The LLM provides reasoning and natural language understanding, while the agent framework adds tool access, state management, error handling, and governance controls. The model alone cannot query a database, send an email, or update a record — the agent's tool-calling infrastructure enables these capabilities.

A critical design decision in any agent system is the level of autonomy. Fully autonomous agents make all decisions without human input, which is appropriate for low-risk, well-defined tasks. Semi-autonomous agents escalate to humans when confidence is low, stakes are high, or they encounter situations outside their training distribution. Most enterprise deployments fall somewhere on this spectrum, with the appropriate level determined by regulatory requirements, risk tolerance, and the maturity of the underlying system.

Most AI applications start with ad-hoc prompt concatenation: a system message, some user input, maybe some retrieved documents, all stitched together as a string. This works for prototypes but breaks down at scale. MCP addresses this by defining a structured, deterministic approach to context assembly — treating the model's input as a governed pipeline rather than an improvised string.

The protocol operates at multiple levels. At the data level, MCP defines how retrieved documents, user messages, and system state are normalized and formatted. At the policy level, it enforces which data an agent can access, what PII must be filtered, and what compliance constraints apply. At the orchestration level, it manages token budgets — deciding what fits in the context window and what gets summarized, pruned, or deferred.

A well-implemented MCP makes model behavior reproducible and auditable. Given the same inputs and policies, the same context is assembled every time. This is essential for regulated environments where you need to explain why a model produced a specific output, and for debugging production issues where non-deterministic context assembly makes root cause analysis nearly impossible.

Context windows have expanded dramatically — from 4K tokens in early GPT models to 200K+ in current Claude models and over 1M in some configurations. But bigger windows don't eliminate the fundamental constraint. Even with a million-token window, you must decide what goes in and what stays out. The cost of filling a large context window is significant, and attention quality can degrade with excessive context.

In production systems, context window management is an engineering discipline. It involves prioritizing what information is most relevant to the current task, compressing verbose inputs through summarization, managing the trade-off between breadth of context and depth of attention, and keeping costs proportional to the value of each inference call.

A common mistake is treating the context window as unlimited storage. Even when technically possible, stuffing the entire context window with retrieved documents often degrades output quality compared to carefully selecting the most relevant subset. The model's attention mechanism works best when the signal-to-noise ratio is high.

Tool calling transforms a language model from a text-in-text-out system into something that can interact with the real world. When an agent calls a tool, it generates a structured request (typically JSON) specifying which function to invoke and with what parameters. The runtime environment executes the tool and returns the results to the agent, which then incorporates them into its reasoning.

The design of tool interfaces is critical. Poorly described tools lead to incorrect invocations. Overly permissive tools create security risks. The best practice is to define tools with clear descriptions, typed parameters, explicit constraints on valid inputs, and well-documented error responses. Each tool should do one thing well, following the same single-responsibility principle that applies to software engineering generally.

Security is the primary concern with tool calling in production. Every tool invocation is a potential attack surface — an agent might be manipulated through prompt injection to invoke tools inappropriately, or a compromised tool could return adversarial data. Defense-in-depth strategies include input validation before tool execution, output sanitization after tool returns, allowlisting of permitted tool-parameter combinations, and comprehensive audit logging of every tool call.

Memory is what turns a stateless language model into a persistent agent. Without memory, every interaction starts from scratch — the agent has no knowledge of what happened before, what decisions were made, or what the user's preferences are. Memory bridges this gap by providing mechanisms to store, index, and retrieve information across sessions.

There are several memory architectures in practice. Conversation buffers store recent messages for short-term continuity. Vector stores enable semantic search over past interactions and knowledge. Graph databases capture relationships between entities and concepts. Structured databases maintain factual records and state. Most production agents use a combination: short-term buffer for immediate context, semantic memory for relevant history, and structured storage for facts and preferences.

Memory introduces its own challenges. What to remember and what to forget is a non-trivial problem — storing everything is expensive and introduces noise, while aggressive pruning risks losing important context. Memory must also be secured: sensitive information stored in memory needs access control, and memory systems must handle conflicting or outdated information gracefully. In regulated environments, memory retention policies must comply with data protection regulations like GDPR's right to erasure.