Building AI Agents: Core Components

At the heart of every modern AI agent is a large language model that serves as the reasoning engine. The LLM interprets instructions, breaks down complex goals into sub-tasks, evaluates options, and generates the natural language or structured outputs that drive downstream actions.

The choice of LLM matters significantly. Larger models like GPT-5 or Gemini 2.5 Pro offer superior reasoning but at higher latency and cost. Smaller models like GPT-5-nano or Gemini 2.5 Flash-Lite are faster and cheaper but may struggle with complex multi-step reasoning. The optimal choice depends on the agent's task complexity, latency requirements, and budget constraints.

Key considerations:

• Model selection should match task complexity: do not over-provision
• System prompts define the agent's persona, constraints, and behavioral boundaries
• Temperature and sampling parameters significantly affect reliability
• Structured output modes (JSON, function calling) improve downstream parsing

Without memory, an agent is simply a stateless model answering one question at a time. Memory gives agents the ability to maintain context, learn from past interactions, and build knowledge over time.

Short-term memory (working memory):

• The current conversation or task context
• Typically implemented as a sliding window of recent messages
• Limited by the LLM's context window (ranging from 8K to 1M+ tokens depending on the model)

Long-term memory (persistent memory):

• Stored externally in vector databases, relational databases, or knowledge graphs
• Retrieved via semantic search (RAG: Retrieval-Augmented Generation) or explicit queries
• Enables the agent to recall past decisions, user preferences, and historical context across sessions

Episodic memory:

• Records of specific past experiences and their outcomes
• Enables the agent to learn from successes and failures
• Critical for self-improvement and avoiding repeated mistakes

Tools are what give agents the ability to act on the world rather than just reason about it. A tool is any external capability the agent can invoke: an API call, a database query, a web search, a code execution environment, a file system operation, or a communication channel.

Modern agent frameworks implement tool use through function calling: the LLM generates a structured representation of the function it wants to call (name, parameters), the framework executes the function, and the result is fed back to the LLM for further reasoning.

Common tool categories:

• Information retrieval: Web search, database queries, document retrieval
• Data manipulation: Spreadsheet operations, data transformation, calculations
• Communication: Email sending, Slack messaging, notification dispatch
• System interaction: API calls, file I/O, code execution
• Specialized: CRM updates, ticket creation, calendar management

Planning is the component that transforms a high-level goal into a sequence of executable steps. This is where the agent's reasoning capability is most critical, and where failures are most consequential.

Common planning approaches:

• ReAct (Reasoning + Acting): The agent alternates between reasoning steps (thinking about what to do) and action steps (doing it). Simple, transparent, and widely used.
• Plan-then-Execute: The agent generates a complete plan upfront, then executes it step by step. Better for well-defined tasks but brittle when plans need to adapt.
• Tree of Thought: The agent explores multiple reasoning paths simultaneously, evaluating and pruning as it goes. More robust but computationally expensive.
• Hierarchical Planning: Complex goals are decomposed into sub-goals, each potentially handled by specialized sub-agents. Essential for multi-agent systems.

Guardrails are the constraints that keep an agent operating within acceptable boundaries. Without them, an autonomous system with tool access can cause significant unintended harm.

Essential guardrail categories:

• Input validation: Sanitize and validate all inputs before processing
• Output filtering: Check agent outputs for harmful, biased, or inappropriate content
• Action boundaries: Restrict which tools the agent can use and under what conditions
• Budget controls: Limit API calls, compute usage, and financial transactions
• Human-in-the-loop: Require human approval for high-stakes decisions
• Audit logging: Record all agent actions, decisions, and reasoning for review