Built My Own MCP Server in 80 Lines of Python. Now Claude Knows My Sales Pipeline.
A year ago I wrote about building a tiny AI agent that uses MCP tools. It’s still the most-read thing I’ve published.
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A year ago I wrote about building a tiny AI agent that uses MCP tools. It’s still the most-read thing I’ve published.
OpenAI, Anthropic, and Google each use a different request and response shape for the same tool. Agent frameworks handle that difference in different places.
Multi-model orchestration is three layers. Workflow orchestration is planning, state, memory, and delegation, and LangGraph and CrewAI are built for it. Model routing is choosing a model per call and falling back when it fails, and provider routing is choosing which provider serves that model.
Building an AI agent is getting easier. Running one reliably in production is still the hard part. At Interrupt 2026, LangChain introduced a major set of updates focused on production agent infrastructure.
How we built a model router into Open SWE's harness that cut median cost per coding task by 64% with no measurable drop in quality, and how to build your own.
Ambient agents respond to events such as an Amazon S3 upload, a schedule, or an alert instead of waiting for a chat prompt. This post walks through building framework-agnostic ambient agents on Amazon Bedrock AgentCore using Amazon SQS, AWS Lambda, and Amazon DynamoDB, with a single ask_human tool…
Every AI pipeline runs on small decisions. Is this ticket about billing or shipping? Does this prompt need the expensive model? Should this agent be allowed to delete that file? Most teams hand each of those calls to a frontier model like ChatGPT. At 10 requests a day, nobody notices.
Most AI workflows run on a single model. Every step goes to the same large language model, whether the task is drafting a customer reply or answering a simple question like "Is this ticket urgent?" That setup gets expensive fast.
Best OpenClaw alternatives compared: BetterClaw, Hermes, NanoClaw, ZeroClaw, n8n, Flowise, Langflow, PicoClaw and Grok Bot, with honest trade-offs.
A CrewAI crew writes our daily pharma brief for three cents One daily Pharma brief from a three-agent CrewAI crew costs $0.030 on Claude Haiku 4.5 when the tool hands the model the news API's key sentences: 14,452 prompt tokens, 3,042 completion tokens, 4 LLM calls, 8 stories with 8 real links.
If you give an LLM access to your code, you write tools. A tool is a function plus what a model needs to call it: a name, a description, and a schema for the arguments. Then you write the same tools again. The first version uses the AI SDK's tool().
Learn what Jev actually is, how System 1 models work, and how to use them inside your software architecture instead of relying on token-generating LLMs for simple decisions.
A Customer Request Is Not a Booking: Designing Memory for AI Assistants “Could we do Friday morning instead?” is enough to change a customer’s preference. It is not enough to put Friday on the …
Connect a LangGraph agent to Apify through the official langchain-apify integration or Apify MCP Server, and give it real-time web search, scraping, and social media data.
You’re here because you already know you need to monitor your AI agents. Maybe you’ve looked at LangSmith, Langfuse, Arize, or Braintrust, and now you’re...
Compare 11 AI observability tools for LLM tracing and monitoring in 2026: tracing model, OpenTelemetry support, self-hosting, and which tools changed owners.
Your users know the answer is somewhere in the docs, but they’re still stuck. Build an AI chatbot that helps them find the right instructions and figure out what to do next.
Today's haul: 16 tools · 14 resources · 20 reads · 20 numbers · 35 things that happened. Every tool, resource and read below has a working link. No link, no listing.
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