AI Agents and Tool Calling Explained: Architecture, Context Engineering, and Implementation Strategies

Source: Dev.to

Large language models have transcended their original functions of summarizing documents and composing emails. These advanced systems now drive AI agents that execute sophisticated tasks such as crafting intricate SQL queries, recommending deploy‑ready code modifications, conducting online searches, analyzing bill photographs for expense automation, and connecting with third‑party applications to handle data in automated workflows. AI‑agent tools have become essential resources for organizations regardless of technical expertise. The ecosystem has grown from early platforms like ChatGPT to comprehensive solutions including the Model Context Protocol, GitHub Copilot, Claude Code, and Cursor. These systems handle everything from data consolidation to intelligent code editing, with specialized agents for SQL databases, spreadsheet applications, and many other domains.

What Are AI Agents?

AI agents are autonomous systems built on large language models that can plan, analyze, and select appropriate tools to achieve objectives. Unlike conventional workflows that require human oversight at every stage, agents adopt a goal‑driven approach, constructing their own execution strategies. When faced with identical challenges, agents may follow different paths depending on the contextual information and resources available.

Example: Building a Website from a Repository

Instead of manually engineering prompts or orchestrating steps, you simply define the desired outcome and provide the necessary resources (e.g., a GitHub repository). The agent independently:

1. Formulates a strategy.
2. Determines the required actions.
3. Executes the task through adaptive reasoning.

Core Architectural Components

AI agents extend basic language‑model interactions with several sophisticated elements:

Retrieval

Agents can query vector databases or other knowledge stores using semantic search. This capability supplies current information, reducing reliance on outdated training data and enhancing factual accuracy.

Memory

By retaining context throughout a conversation, agents reduce inaccuracies and improve response consistency during complex, multi‑step tasks. Memory prevents the loss of critical details that would otherwise require re‑prompting.

Tool Calling

Tool calling mirrors human problem‑solving: conducting web searches, querying SQL databases, creating images, or executing Python code. Tools are defined during agent initialization with detailed descriptions of their capabilities and recommended use cases.

Planning

Planning works hand‑in‑hand with memory. Agents can:

• Examine errors,
• Alter their course of action,
• Integrate fresh information from web searches or documentation.

This adaptive planning keeps agents responsive to changing conditions and emerging challenges throughout task completion.

Managing Context Size

Each architectural component contributes tokens to the agent’s context window, creating memory‑management challenges. As contextual information grows, agents may only attend to a portion (e.g., ~60 %) before making decisions, risking overlooked details or unreliable responses.

Strategic Context Engineering

Developers should:

1. Organize information clearly – supply only critical details needed for the current task.
2. Store supplementary data in metadata or external knowledge repositories.
3. Retrieve on demand – keep the active context manageable while ensuring comprehensive information is accessible when required.

Effective context engineering directly influences performance, preventing information overload that leads to confused outputs.

Tool Integration Pattern

Tools act as extensions of the agent’s native capabilities, bridging language‑model reasoning and practical action. The interaction follows a structured cycle:

1. Interpret user request – determine if external tools are needed.
2. Select the appropriate tool – based on the tool descriptions provided at initialization.
3. Format parameters and execute the tool call.
4. Process the returned information – incorporate it into ongoing reasoning.

This loop may repeat multiple times as the agent progresses toward complex objectives, with each invocation building on previous results.

Frameworks for Building AI Agents (Python)

Several open‑source frameworks help developers construct AI agents from the ground up, each offering distinct trade‑offs:

Choosing the right framework depends on project complexity, team expertise, integration needs, and the specific tools the agent must access.

No‑Code Alternatives

For technical professionals with limited coding experience, no‑code platforms provide a faster, more accessible route to building agentic workflows. FME by Safe Software is a notable example:

• Visual interface for aggregating data from disparate sources with varying schemas.
• Built‑in transformations to standardize and structure data.
• Ability to incorporate AI‑agent logic and tool calls without writing code.

No‑code solutions accelerate implementation speed and broaden participation, making AI‑agent capabilities available to a wider audience.

Conclusion

AI agents combine memory, retrieval, dynamic planning, and tool calling to operate autonomously toward defined goals. Effective context engineering and thoughtful tool integration are essential for reliable performance. Whether built with Python frameworks like LangGraph or assembled on no‑code platforms such as FME, AI agents are rapidly becoming indispensable components of modern data‑driven workflows.