An examination of the hype surrounding autonomous AI agent frameworks and why they may add unnecessary complexity to software development. The author argues that for most production use cases, structured workflows using LLM function calling are more reliable than fully autonomous agents.
- Complexity vs control in agentic systems
- Limitations of current models regarding long-term autonomy
- Advantages of explicit programming over unpredictable loops
- Complexity vs control in agentic systems
- Limitations of current models regarding long-term autonomy
- Advantages of explicit programming over unpredictable loops
This tutorial demonstrates how to build a local, privacy-first tool-calling agent using the Google Gemma 4 model family and Ollama. It explains the transition from static language models to dynamic autonomous agents through function calling, allowing models to interact with external APIs and real-world data. The guide provides a practical Python implementation using a zero-dependency approach to create tools for weather retrieval, news fetching, time checking, and currency conversion.
- Overview of the Gemma 4 model family and its native agentic capabilities.
- The architectural shift from closed-loop conversationalists to tool-enabled agents.
- Setting up a local inference environment using Ollama and the gemma4:e2b model.
- Implementing Python functions and mapping them to JSON schemas for model instruction.
- Orchestrating the agentic workflow loop to execute tools and synthesize live context.
- Overview of the Gemma 4 model family and its native agentic capabilities.
- The architectural shift from closed-loop conversationalists to tool-enabled agents.
- Setting up a local inference environment using Ollama and the gemma4:e2b model.
- Implementing Python functions and mapping them to JSON schemas for model instruction.
- Orchestrating the agentic workflow loop to execute tools and synthesize live context.
This article compares Model Context Protocol (MCP), Function Calling, and OpenAPI Tools for integrating tools and resources with language models, outlining their strengths, limits, security considerations, and ideal use cases.
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
This article provides a hands-on guide to Anthropic’s Model Context Protocol (MCP), an open protocol designed to standardize connections between AI systems and data sources. It covers how to set up and use MCP with Claude Desktop and Open WebUI, along with potential challenges and future developments.
This article details the author's insights into AI function calling, its challenges, and the Agentica framework developed to address them, emphasizing the importance of JSON schema understanding, compiler support, and a document-driven approach.
This article details a comparison between Model Context Protocol (MCP) and Function Calling, two methods for integrating Large Language Models (LLMs) with external systems. It covers their architectures, security models, scalability, and suitable use cases, highlighting the strengths and weaknesses of each approach.
MCP is best suited for robust, complex applications within secure enterprise environments, while Function Calling excels in straightforward, dynamic task execution scenarios. The choice depends on the specific needs, security requirements, scalability needs, and resource availability of the project.
MCP is best suited for robust, complex applications within secure enterprise environments, while Function Calling excels in straightforward, dynamic task execution scenarios. The choice depends on the specific needs, security requirements, scalability needs, and resource availability of the project.
The author discusses the development of a function calling large language model (LLM) that significantly improves latency for agentic applications. This LLM matches or even exceeds the performance of other frontier LLMs. It is integrated into an open-source intelligent gateway for agentic applications, allowing developers to focus on more differentiated aspects of their projects. The model and the gateway are available on Hugging Face and GitHub, respectively.
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