Automation

atflux

Agents when they earn the keys, scripts when that is enough

Pipelines, evaluations, and the paperwork around both

What I Offer

AI Agent Harnesses

Custom agent architectures using tools like Cursor, Claude Code, and the Agent SDK. Designed for real-world productivity, not demos.

RAG Systems

Chunking, embeddings, and citation hygiene so answers point back to the file that said it — not a confabulation dressed as confidence.

Automation Pipelines

Cron plus queue plus dead-letter, or GitHub Actions glue — whatever stops humans from copy-pasting between tabs.

Prompt Engineering

System cards, tool schemas, refusal boundaries — the unsexy scaffolding that keeps agents from improvising invoices.

Consultancy

Vendor demos decoded: what plugs into your SSO, what needs a GPU budget, what is a press release.

Integration

Connecting AI capabilities with existing systems. APIs, webhooks, event-driven architectures, and data pipelines.

Approach

1

Name the failure mode

Is the pain manual data entry, hallucinated support answers, or “we bought seats and nobody uses them”? Different failures get different tools.

2

Measure the boring numbers

Latency to first token, weekly tool errors, pounds per thousand rows — pick one spine metric so we know whether a change earned its deploy.

3

Cut scope like you mean it

First release should embarrass you slightly in features, not in reliability. Expand when operators ask for more, not when the slide deck does.

Automation or agents

What's eating time, how often it runs, any hard deadline, and your budget or range? Let's find out if we're the right fit together!