Notes from building with AI agents

How to add AI to an existing SaaS product: start with one reviewed task, permission-bound sources and a clear stop rule, not a wholesale rebuild.

Before you ask a software house to quote your MVP, agree one first-release outcome, what stays out and how you will check delivery. A worked example with eight checks and four disputed requests.

Two AI agents built on Hermes Agent work at Awakast like a pair of developers: one writes code, the other reviews it, and an engineer accepts the result. Here is how it played out on a real change.

Choosing a development team that works with AI agents? What ours may finish alone, what waits for a person, and two events on our own site: a failed release we restored and retried, and a demo that loaded analytics before visitors had agreed to it.

AI agents are shifting from demo tools to operational infrastructure for teams that need faster decisions and lower manual workload.

A prototype that works in a demo can still be the wrong thing to launch. Use this practical framework to decide what needs an engineering review before real users, payments or sensitive data raise the stakes.

Accept an AI discovery sprint on evidence, not a demo. Use a handover worksheet to decide what to build, integrate, change or stop before funding delivery.

Custom AI solutions or an off-the-shelf tool? Test one workflow against the tool you already have, then commission only the layer it cannot cover.

A practical path from a working AI-built prototype to accountable software: eight release gates that make the right risks visible before customers, money and product ownership are involved.

Tech Show London confirmed a clear market shift: teams are moving from AI curiosity to production implementation with real accountability.

Awakast won the main Tech Contest award at Made in Wroclaw 2025 with ValidLabels, a product built around fast multilingual compliance labeling.

In Lisbon, the strongest signal was clear: AI teams are rewarded for execution clarity and measurable outcomes, not feature volume.

Vancouver reinforced demand for practical multi-agent and voice AI implementations where speed and quality control must coexist.