New agent architecture cuts scenario setup time by 42%

Most companies have already tried to launch AI agents, and almost all hit the same wall: every new scenario needs its own tool setup, access rights and checks. Time goes into connecting things, not into logic — and a pilot stretches into weeks.

The Korveniq AI team rebuilt its agent architecture around modular blocks. The model, tools, memory and validation rules are now described separately and combined into a scenario without touching integrations. Across internal and pilot deployments, average scenario setup time dropped by 42%.

From a monolithic agent to a set of modules

Agents used to be built as one piece: prompt, API calls and business rules were intertwined, and any change needed a developer. In the new architecture each part has its own responsibility and its own version.

An orchestrator distributes steps between agents, while tools connect through a single integration layer. If a team changes its CRM or model, scenarios stay the same — only the relevant module is updated.

What the pilot launches showed

The architecture was tested on request handling, lead qualification and report preparation. In every case the main gain came from reuse: ready-made tools and checks moved between scenarios without rework.

The role of business teams changed too. They assemble and adjust scenarios themselves, while developers join only for new integrations and complex exceptions.

Reliability and control

Every agent step is logged: inputs, tools called and the final decision. Critical actions go through rule checks or human approval, and a scenario is tested on historical data before launch.

The model can be swapped without rebuilding the scenario — for a faster one on simple tasks or a more accurate one on complex ones. This keeps cost, speed and quality in balance.

Every scenario is assembled from versioned modules with a transparent execution log.

What's next

Upcoming Korveniq AI releases will add a library of ready-made scenarios, automatic quality evaluation before publishing and extended per-run cost analytics.

Teams can start with one priority process: Korveniq specialists will help pick a scenario, assemble the first circuit from modules and measure the effect on real data.