#Tool Use

Articles about Tool Use — exploring patterns, best practices, and real-world implementations in production systems.

9 posts tagged with tool use. ← All posts

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Pratik Dhanave · ·11 min read

Tool Calling with watsonx

Letting a Granite model on watsonx.ai invoke your Python functions — the full request-and-response loop with the first-party `ibm-watsonx-ai` chat API, plus the shorter LangChain path with `ChatWatsonx.bind_tools`.

Function/tool calling against watsonx.ai Granite models in Python: the chat() round-trip loop (json.loads the arguments, echo tool_call_id, cap iterations), handling multiple tool calls, and the LangChain ChatWatsonx.bind_tools path — Granite models are tool-trained.

#IBM watsonx#Granite#Tool Use#Python#AI Engineering#Software Engineering
Pratik Dhanave · ·12 min read

Tool Calling with NIM

Driving function calling against NVIDIA NIM models from Python — the full request-execute-respond loop with the plain `openai` client, then the same thing automated by `ChatNVIDIA.bind_tools`.

Function/tool calling against NIM models in Python: the openai round-trip loop (json.loads the arguments string, echo tool_call_id, cap the iterations) and the LangChain ChatNVIDIA.bind_tools path — with the honest caveat that model support varies.

#NVIDIA#NIM#Tool Use#Python#AI Engineering#Software Engineering
Pratik Dhanave · ·12 min read

Tool Use with the Converse API

How to give an Amazon Bedrock model real Go functions — declaring tools, catching the tool-use stop reason, executing your code, and returning results — using the full round-trip loop in aws-sdk-go-v2.

Giving a Bedrock model tools in Go via the Converse API: declaring a ToolConfiguration, the ToolUse round-trip loop, echoing ToolUseId, returning tool results as a user message, and handling parallel tool calls.

#Amazon Bedrock#AWS#Tool Use#Go#AI Engineering#Backend Engineering#APIs
Pratik Dhanave · ·14 min read

Agents from Scratch

Building a real agent loop in Go by hand — an LLM in a loop that picks tools, runs them, reads the results, and repeats until the task is done — so you can see there is no magic behind LangGraph, MAF, or ADK.

Build a minimal but real agent loop in Go by hand: an Agent with a tool registry and a reason-act Run loop, an iteration budget, validation against hallucinated tools, feeding tool errors back as observations, and parallel tool calls — the loop frameworks formalize, demystified.

#AI Engineering#AI Agents#Go#Tool Use#Backend Engineering
Pratik Dhanave · ·13 min read

Structured Output and Tool Calling

From-scratch Go for the two mechanisms that turn an LLM from a text generator into a component you can wire into real software — schema-constrained JSON and function calling — both spoken over the same OpenAI-compatible chat JSON.

Getting reliable machine-readable output from an LLM in Go: structured output (json-schema mode, decode into a typed struct, validate with a bounded retry) and tool/function calling (the full round-trip loop, decoding tool arguments, returning results tied to the call id).

#AI Engineering#LLMs#Tool Use#Go#Backend Engineering
Pratik Dhanave · ·5 min read

Tell the Agent What It's Allowed to Do

If your gateway will block a tool call, don't make the agent discover that by trying. Hand it a capabilities brief and stop paying for turns it can't complete.

If your gateway will block a tool call, don't make the agent find out by trying, because each blocked attempt is a wasted turn. A capabilities brief aligns what the model thinks it can do with what enforcement actually allows.

#Agentic AI#Guardrails#Cost Optimisation#Tool Use
Pratik Dhanave · ·5 min read

Planners & Thinking: Making an ADK Agent Reason Before It Acts

Post 23 of 26 in "Google ADK, Concept by Concept" — how a planner turns one-shot answers into inspectable plan-then-act reasoning.

Structuring an agent's reasoning: planners that make the model plan-then-act (ReAct-style), the built-in thinking feature, and how a planner improves multi-step tool use over naive prompting.

#Google ADK#AI Agents#Python#Go#Software Engineering#Backend Engineering#Prompt Engineering#Tool Use

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.