Latest Blogs.

Enterprise AI agents: what changes at scale
The prototype agent and the enterprise one solve the same task with a different architecture underneath. SSO, audit, governance, and the review nobody skips.

Building AI agents that can safely modify customer data
Read access is forgiving of mistakes. Write access is not. The permission model, approval flow, and audit trail that make a write-capable agent safe to ship.

LangGraph vs CrewAI vs AutoGen: a practical comparison
Three popular agent frameworks, compared on what actually differs in production: control over the loop, debugging, and how much they decide for you.

What are AI agents, actually?
Not a chatbot, and not magic: an AI agent is a model that can call tools and decide what to do next. What that definition includes, excludes, and implies.

How to architect an AI agent platform
Not one agent behind an API: a platform. The layers that stay stable while models and frameworks underneath them change, and the order to build them in.

How much does an AI agent cost to build?
The honest range for a production AI agent, broken into the build, the harness work nobody quotes for, and the per-action running cost once it ships.

Agent harness: what it is, and why your agent needs one
An AI agent is a model plus a harness. The model is rented; the harness is what you own, and it is where agents succeed or fail in production. What goes in one.

MCP servers: exposing your systems to models safely
The Model Context Protocol is now how tools reach a model. How we design an MCP server for a client system: scope, safety, and what to leave out at first.

Evals before features: how we score AI work
The first thing we build on any AI feature is the test set, not the feature. How the evaluation set is made, what it measures, and why it changes the build.
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