The Complete AI Agent Handbook: Architecture, Workflows, and Tooling

In early AI integrations, LLMs were treated as static request-response handlers. A user submitted a prompt, and the model returned an output. Today, the industry has shifted to agentic workflows—loops where an AI model evaluates feedback, calls external tools, reads databases, interacts with browser sessions, and refines its output until a high-level goal is achieved.

This handbook is a comprehensive engineering guide detailing the components, architectures, and tools used to build reliable AI agents.

1. What is an AI Agent?

Unlike a chatbot or a static automation script, an AI agent operates in a loop consisting of:

$$\text \longrightarrow \text \longrightarrow \text \longrightarrow \text$$

1. Observe: Gather environmental inputs (user prompts, database states, CLI execution logs, or browser DOM trees).
2. Plan: Evaluate options, decompose complex goals into discrete sub-tasks, and select tools.
3. Act: Invoke tools (execute SQL, call APIs, click browser coordinates, write code).
4. Verify: Check the output of actions against success criteria. If the output fails, backtrack or retry with adjusted parameters.

2. Core Architectural Pillars

2.1 Tool Use (Function Calling)

An agent accesses the outside world via tool schemas. Tools are presented to models as structured JSON schemas:

{
  "name": "read_user_profile",
  "description": "Reads user details from DB based on ID",
  "parameters": {
    "type": "object",
    "properties": {
      "user_id": { "type": "string" }
    },
    "required": ["user_id"]
  }
}

2.2 Model Context Protocol (MCP)

Developed by Anthropic, MCP is an open standard that enables models to connect to local databases, shell interfaces, and secure filesystems. Instead of writing custom API adapters for every tool, you can deploy pre-built MCP servers (such as PostgreSQL, SQLite, or Puppeteer) that expose tools directly to your agent.

2.3 Memory Systems

Agents require two types of memory:

• Short-Term Memory: Managed by updating conversational context buffers and passing thread history across loops.
• Long-Term Memory: Persistent storage of user configurations, business rules, or past action outputs stored in PostgreSQL databases or vector stores.

3. Designing Multi-Agent Systems

When a single agent is tasked with a highly complex workflow (e.g., writing code, reviewing code, and deploying to AWS), the accuracy rates degrade. The solution is to create specialized agents that collaborate.

               ┌────────────────┐
               │ Planner Agent  │
               └───────┬────────┘
                       │ (Delegates)
       ┌───────────────┼───────────────┐
       ▼               ▼               ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Research    │ │ Coding      │ │ Testing     │
│ Agent       │ │ Agent       │ │ Agent       │
└─────────────┘ └─────────────┘ └─────────────┘

By assigning strict roles (e.g., "Developer Agent" with shell execution access, "QA Agent" with test suite access, and "Reviewer Agent" with static analyzer access) and using frameworks like LangGraph or CrewAI, you build resilient systems that execute in parallel.