AgentNotebook

Tag

#AI agents

3 posts tagged.

From scratch

Build your first AI agent from scratch in 30 minutes

An AI agent is just a loop: you call a model, the model asks to run a tool, you run it, you feed the result back, and you repeat until the model is done. In this tutorial you build that loop yourself in plain TypeScript against the Anthropic Messages API — no framework. You will wire up two tools (read a file, run a calculation), let the model orchestrate them, add a turn cap and basic guardrails, then verify the whole thing end to end. The result is a small research agent you fully understand and can extend with your own tools.

12 min read4
Add to SaaS

Add an AI agent to an existing SaaS without rewriting it

You do not need to rebuild your product to ship an AI agent inside it. The trick is to expose the service functions you already have — search records, create an order, fetch a customer — as tools, then run a small server-side agent loop that the model uses to orchestrate them. This tutorial wraps an existing service layer as tools, scopes every call to the authenticated user, separates safe read tools from gated write tools, exposes the agent as one authenticated endpoint, and deploys that endpoint to Totalum. Your database, auth, and business logic stay untouched.

13 min read4
Eval

Agent eval methodology: 5 metrics that actually catch regressions

Agents fail quietly: a prompt tweak that fixes one task often breaks three others, and manual spot-checks never re-test what used to work. The fix is a frozen eval set scored on every change. This tutorial builds that harness and tracks five metrics that actually catch regressions — task success rate, tool-call accuracy, step efficiency, cost per task, and a safety/guardrail rate. You will assemble an eval set, write a runner that scores each metric, and turn the before/after diff into a regression gate so a change only ships when the numbers hold or improve.

13 min read6