RSV Global / Services / AI Agent Development
Agents that do recurring work,
not agents that answer a prompt.
Stateful agents with tools, memory, evaluation, retry policy and explicit action boundaries. The model is one component. The durable system is the state, evidence, permission and quality layer around it.
Everything around the model, which is where the work is.
Architecture first. AI compresses implementation afterwards.
The workflow, boundaries and acceptance criteria are written down before implementation. That lets AI accelerate delivery without quietly becoming the architect.
Define good
Write down what an acceptable output is before building anything that produces one.
Separate the stages
Research, state, generation, evaluation and action as distinct steps, each independently changeable.
Place the human
Identify the consequential boundaries and put approval there, not everywhere.
Run and tighten
Operate it, collect the failures, and turn them into evaluation criteria.
We will not build an agent where good output cannot be defined.
If nobody can write down what an acceptable result looks like, no evaluation can be built, no failure can be detected, and the system cannot be trusted to act. That is a specification problem, and shipping an agent on top of it makes it worse.
Systems we built and run ourselves.
Not client work. These are our own products, carrying our own risk, which is why the boundaries sit where they do.
Nirantar
Multipart grading against named criteria, with mandatory failures triggering bounded revision and the evaluation record preserved rather than discarded.
LeaseOasis market intelligence
Rolling no-repeat memory and geographic scoping, producing one session of output across three channels without duplication.
Not quite the right door?
Show us the workflow everyone has learned to tolerate.
We will help determine whether it needs a focused product, an autonomous operating system, a stronger data layer, a review of what already exists, or a simpler fix that involves no AI at all.