Input guardrails check whether a user prompt is safe. Output guardrails check whether a model response is appropriate. Agent guardrails check whether the actions an agent takes are within bounds. Thes

Machine learning teams face a version control problem that Git does not solve. Git tracks code changes, but ML experiments change more than code. They change hyperparameters, datasets, model architect

A single agent can already fail in difficult ways: a tool times out after applying a change, retrieved evidence is stale, or an answer invents a policy. With one decision loop, there are fewer handoff

The Model Context Protocol gives AI agents a standardised way to discover and invoke external tools. In development, MCP works well with a local server running on localhost and a handful of tools. The

A healthcare analytics company received notice on a Tuesday afternoon that their primary AI infrastructure vendor was filing for Chapter 7 bankruptcy. The platform hosted their patient risk stratifica

Traditional application observability focuses on three signals: request latency, error rates, and resource utilisation. If the request returns a 200 in under two hundred milliseconds, the system is he

A critical open-source library used by thousands of companies, including several Fortune 500 firms, is maintained by one person in their spare time. This is not a hypothetical. It is a description of

When an AI agent can call external tools, the security boundary shifts from the model to the tool layer. The model generates a request to call a tool. The tool executes against real systems, reading d

The guardrail problem in AI is a tension between two failure modes. Too few guardrails and the system produces harmful, inaccurate, or brand-damaging outputs. Too many guardrails and the system refuse