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Practical stories, product lessons, teardowns, and field notes for independent founders.

AI Agent API Cost Controls: How to Prevent Runaway Spending

A looping agent can turn an inexpensive API integration into an unexpected bill.

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A looping agent can turn an inexpensive API integration into an unexpected bill.

A practical architecture for giving AI agents useful tool access without turning the model into a production superuser.

The practical AI stack for research, coding, product work, content, and automation when you are building with a small team.

Choosing authentication for an agent is different from choosing authentication for a normal backend integration.

Memory is not one database. Separate run context, session state, durable facts, and long-term recall.

Tool calling is the bridge between model reasoning and real application actions. The server still owns validation and authorization.

Structured function calling does not make arguments trustworthy. Every tool input still needs server-side validation.

A production agent is a software system around a model. Separate reasoning, tools, state, permissions, approvals, and observability.

Product & SaaS

How to choose infrastructure for a small SaaS without paying for enterprise complexity before you need it.

OpenAI's current agent stack has three paths. Choose the runtime that matches the workflow instead of starting with the most sophisticated option.