1046 posts · Page 36 of 88. ← Blog
Your first real inference calls with the ibm-watsonx-ai SDK — credentials, ModelInference, the generate and chat APIs, streaming, token usage, error handling, and the langchain-ibm path.
Make your first watsonx.ai calls in Python: credentials (IAM key + project_id + regional url), ModelInference with generate_text vs the modern chat API, decoding methods and parameters, streaming, token usage, and the langchain-ibm ChatWatsonx path.
Your first real NVIDIA NIM calls in Python, two idiomatic ways: the plain OpenAI SDK pointed at NVIDIA's endpoint, and the official LangChain integration — with error handling, streaming, and the one change that makes the same code run against a self-hosted model.
Make your first NIM calls in Python two ways: the standard openai client pointed at the API Catalog with an nvapi- key, and the official ChatNVIDIA from langchain-nvidia-ai-endpoints — with streaming, token usage, typed errors, and the base_url swap to a self-hosted NIM.
How to stream Amazon Bedrock responses token-by-token with the aws-sdk-go-v2 Converse API, decode the event stream with a double type-switch, and account for tokens and cost from the metadata event — accurately, in Go.
Streaming responses and accounting for tokens and cost on Bedrock in Go: ranging the ConverseStream event stream, the nested delta unions, checking stream.Err(), and computing cost from the metadata usage event with a formula you fill in.
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.
How to score an agent's final answer against a reference — from exact string match, through ROUGE-1 unigram overlap, to an LLM judge — with original Go you can drop into a test suite. Part 5 of Evaluating Agents in Go.
How to score an agent's final answer against a reference — from exact string match, through ROUGE-1 unigram overlap, to an LLM judge — with original Go you can drop into a test suite. Part 5 of Evaluating...
From the smallest possible agent to a browsable service — the core loop, the four ways to run it, how memory and tools attach, and two ways to put a server in front of it.
From the smallest possible agent to a browsable service — the core loop, the four ways to run it, how memory and tools attach, and two ways to put a server in front of it.
How to wrap an agent run to log, guard, retry, redact, and secure it — using middleware seams that sit entirely outside the agent's own logic.
How to wrap an agent run to log, guard, retry, redact, and secure it — using middleware seams that sit entirely outside the agent's own logic.
A complete guide to what an agent remembers — from a single conversation held in a session, to durable facts injected on every run, to the per-request values that reach a tool without ever touching the model's schema.
A complete guide to what an agent remembers — from a single conversation held in a session, to durable facts injected on every run, to the per-request values that reach a tool without ever...
From a single decorated async function to an explicit graph of executors and agent nodes — the core workflow model in Microsoft Agent Framework, and the two APIs that express it.
From a single decorated async function to an explicit graph of executors and agent nodes — the core workflow model in Microsoft Agent Framework, and the two APIs that express it.
Once you can wire a chain of executors, the graph earns its keep: concurrency, durable state, composition, and control — the patterns that turn a toy pipeline into a system that survives a crash.
Once you can wire a chain of executors, the graph earns its keep: concurrency, durable state, composition, and control — the patterns that turn a toy pipeline into a system that survives a crash.
How to pause a workflow for a human decision, package a whole workflow as an agent, and see exactly what a run did — through OpenTelemetry spans and a rendered graph — in Microsoft Agent Framework.
How to pause a workflow for a human decision, package a whole workflow as an agent, and see exactly what a run did — through OpenTelemetry spans and a rendered graph — in Microsoft Agent Framework.
A complete guide to coordinating many agents — from a fixed pipeline, to parallel fan-out, to a self-routing mesh, to a planner that decides who acts next, to publishing an agent as a network service other agents can call.
A complete guide to coordinating many agents — from a fixed pipeline, to parallel fan-out, to a self-routing mesh, to a planner that decides who acts next, to publishing an agent as a network...