Agent Architecture Basics: How AI Agents Think
An AI agent is more than a chatbot. While a chatbot responds to messages, an agent perceives its environment, makes decisions, and takes actions to achieve goals. Understanding agent architecture is the foundation of building effective AI systems.
The Agent Loop: Every AI agent follows this core loop: 1. Observe: Read the current state (user input, database, file system, API responses) 2. Think: Reason about what to do next (this is the LLM call) 3. Act: Execute an action (call a tool, write a file, send a message) 4. Reflect: Evaluate the result and decide whether the goal is achieved 5. Repeat until the goal is met or the agent determines it cannot proceed
Key components of an AI agent:
- LLM Brain: The language model (Claude, GPT-4) that does the reasoning. This is where intelligence lives.
- Tools: Functions the agent can call — database queries, API requests, file operations, web searches. Tools give the agent hands.
- Memory: Short-term (conversation context) and long-term (stored knowledge, previous interactions). Memory gives the agent continuity.
- Planning: The ability to break complex goals into subtasks and execute them in order. Planning gives the agent strategy.
The simplest possible agent (in pseudocode): ``` while not goal_achieved: observation = get_current_state() action = llm.decide(observation, tools, memory) result = execute(action) memory.add(result) goal_achieved = llm.evaluate(result, goal) ```
This simple loop powers everything from Claude Code to autonomous research agents. The complexity comes from the tools, memory systems, and planning strategies you add.
Tool Use and Function Calling: Giving Agents Hands
Tools transform an AI from a text generator into an agent that can interact with the real world. In 2026, tool use (also called function calling) is the most important AI agent capability.
How tool use works: 1. You define tools with a name, description, and parameter schema (using Zod or JSON Schema) 2. The LLM decides when to call a tool based on the conversation context 3. Your application executes the tool and returns the result to the LLM 4. The LLM uses the result to continue reasoning or respond to the user
Building tools with the Vercel AI SDK:
Define a tool with Zod schema: ``` const weatherTool = tool({ description: 'Get the current weather for a location', parameters: z.object({ location: z.string().describe('City name'), unit: z.enum(['celsius', 'fahrenheit']).optional(), }), execute: async ({ location, unit }) => { const data = await fetchWeatherAPI(location); return { temperature: data.temp, condition: data.condition }; }, }); ```
Essential tools for business agents: - Search tool: Query a knowledge base or the web for information - Database tool: Read and write records (customer data, orders, inventory) - Email tool: Send emails, read inbox, classify messages - Calendar tool: Check availability, schedule meetings - File tool: Read documents, generate reports, create spreadsheets
Tool design best practices: 1. Write clear descriptions — the LLM uses the description to decide when to call the tool 2. Keep parameters simple — fewer parameters means fewer mistakes 3. Return structured data — JSON responses are easier for the LLM to process 4. Add error handling — return clear error messages the LLM can act on 5. Limit scope — each tool should do one thing well
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Memory and State: Giving Agents Continuity
Without memory, every interaction with an AI agent starts from zero. Memory systems give agents the ability to learn from experience, maintain context, and build on previous work.
Three types of agent memory:
1. Conversation Memory (short-term) The current conversation history. Every message from the user and every response from the agent is stored and sent back to the LLM on each turn. This is the simplest form of memory and is built into every chat API. - Limitation: Context windows are finite. A 200K token window holds roughly 100,000 words — plenty for a single session but not for months of history. - Strategy: Summarize older messages to compress context. Keep the last 20 messages in full detail, summarize everything before that.
2. Semantic Memory (long-term knowledge) Facts, documents, and knowledge stored in a vector database. When the agent needs information, it searches this memory using semantic similarity. - Implementation: Embed documents with an embedding model, store in Pinecone or pgvector, retrieve relevant chunks when the agent needs context. - Use case: Customer support agents that search a knowledge base, research agents that remember previous findings.
3. Episodic Memory (experience-based) Records of previous agent interactions — what worked, what failed, what the user preferred. This is the most advanced form of memory. - Implementation: Store interaction summaries with outcomes in a database. Before each new task, retrieve similar past experiences. - Use case: Personal assistants that learn your preferences, coding agents that remember your project conventions.
Memory architecture for production agents: Combine all three types. Use conversation memory for the current session, semantic memory for domain knowledge, and episodic memory for personalization. The agent consults all three before making decisions.
Multi-Agent Systems: Agents That Collaborate
A single agent has limits. Multi-agent systems use multiple specialized agents that collaborate to solve complex problems — just like a team of human specialists.
Why multi-agent systems work: - Specialization: Each agent is an expert in its domain. A coding agent writes code, a testing agent verifies it, a review agent checks quality. - Parallelism: Multiple agents work simultaneously on different subtasks. - Separation of concerns: Each agent has a focused system prompt and tool set. No single agent needs to handle everything.
Common multi-agent architectures:
1. Manager-Worker (most common) - A manager agent receives the goal and delegates subtasks to worker agents - Workers report results back to the manager - The manager coordinates, resolves conflicts, and produces the final output - Example: A content creation system where the manager assigns writing, editing, SEO optimization, and image generation to specialist agents
2. Pipeline (sequential processing) - Each agent processes the output of the previous agent - Agent A researches, Agent B writes, Agent C edits, Agent D publishes - Simple and reliable but no parallelism
3. Debate (quality through disagreement) - Multiple agents independently solve the same problem - A judge agent evaluates the solutions and picks the best one - Used when quality matters more than speed
Building a multi-agent system in TypeScript: - Use separate LLM calls for each agent with different system prompts - Define a message passing protocol between agents (JSON messages with type, content, and metadata) - Implement a coordinator that manages agent lifecycles and communication - Add error handling for agent failures (timeout, hallucination, tool errors)
CodeLeap's advanced module covers multi-agent systems with hands-on projects — you'll build a content pipeline, a code review system, and a customer support escalation agent.
Deployment and Monitoring: Running Agents in Production
Building an agent is half the work. Running it reliably in production is the other half. Production agents need proper deployment, monitoring, and guardrails.
Deployment options for AI agents:
1. Serverless functions (Vercel, AWS Lambda): Best for event-triggered agents that process requests and return results. Low cost, automatic scaling, but limited execution time (10-60 seconds).
2. Long-running services (Railway, Fly.io, VPS): Best for agents that need persistent connections, long execution times, or background processing. More control but more management.
3. Queue-based architecture (BullMQ, SQS): Best for agents that process tasks asynchronously. Reliable, fault-tolerant, and scalable. Agents pull tasks from a queue, process them, and report results.
Monitoring AI agents:
Unlike traditional software, AI agents can fail in unpredictable ways — hallucinating tool calls, infinite loops, or confidently wrong answers. Monitor for:
- Token usage: Track cost per agent run. Set hard limits to prevent runaway costs.
- Latency: Measure time from request to completion. Alert on unusual delays.
- Tool call patterns: Log every tool call with inputs and outputs. Detect abnormal patterns.
- Success rate: Track what percentage of agent tasks complete successfully.
- Hallucination detection: Compare agent outputs against ground truth when possible.
Essential guardrails: 1. Maximum iterations: Cap the agent loop at 10-20 iterations to prevent infinite loops 2. Token budget: Set a maximum token spend per task (e.g., $0.50) 3. Tool restrictions: Limit which tools the agent can call based on the task type 4. Human-in-the-loop: For high-stakes actions (sending emails, making payments), require human approval 5. Audit logging: Record every decision and action for debugging and compliance
The reality: Most production agents fail 5-15% of the time. Design for failure — graceful degradation, clear error messages, and automatic escalation to humans.