AI Agents Development
Autonomous and assisted agents that carry out real work – gather inputs, call your systems, apply your rules and escalate the edge cases, with an audit trail throughout.
Results from work we have shipped
WislaSearch is an AI-powered search assistant that centralises fragmented data, indexes and retrieves the right information instantly, and keeps everything inside your own secure infrastructure.
Agents we build
An agent that runs a task end to end – onboarding, reconciliation, case handling – and escalates what it cannot decide to a person.
Agents that gather and summarise from your documents, databases and APIs, with citations back to the source.
Agents that act through defined, permissioned tools – reading a record, calling an API, filing a ticket – never free-form access to your systems.
Several agents co-ordinated through a controller, each with a narrow job, a clear hand-off and a stop condition.
Agents that propose and a person approves, for decisions that must stay with a human in a regulated operation.
Permissions, spend limits, logging and a full audit trail, so every action an agent takes is reviewable after the fact.
Wiring an agent into the product and the systems you already run, with the monitoring to operate it.
How we deliver AI agents
The same delivery discipline on every engagement - from the first map to a handover your team runs.
We map the task, the tools the agent may use and the actions it must never take, then scope a proof of concept.
We define the agent's tools as explicit, permissioned interfaces – what it can read, what it can change and where it must stop.
We build the agent, constrain it with rules and guardrails, and test it against the cases where an unconstrained agent goes wrong.
Deployed with logging, spend controls and a supervision path, and handed to your team with a runbook.
What shapes the work
An agent that can act needs tighter limits than a model that only advises. The moment software can call your systems and change state, the important questions stop being about accuracy and start being about permission: what may it touch, what must it never do, and how do you prove after the fact what it did.
So we build the guardrails before the autonomy: explicit permissioned tools, spend and rate limits, a stop condition, and an audit trail on every action. The agent gets exactly the reach its task needs and no more.
The reliable pattern for a regulated operation is a model that proposes and deterministic rules that constrain what it can decide. The agent handles the judgement and the language; hard rules handle the limits that cannot be left to a probabilistic model. For the applied modelling behind that, see AI development, and for the language layer many agents lean on, generative AI.
Agents multiply the data-leak surface, because they read and write across systems as they work. We design for the boundary from the start: self-hosted or private-endpoint models where capability allows, your data kept on your infrastructure, and contractual limits on any third-party endpoint. The full boundary approach is on the Data Science &
An agent is worth little until it can reach your systems, so a large part of this work is AI integration: the tools, contracts and monitoring that let an agent act safely. And because most agents reason in language, they build on generative AI. This page keeps to the agent itself – the task, the tools, the guardrails and the audit trail.
Agents carry more risk than a passive model, so we prove the guardrails before the reach. A fixed-price, time-boxed proof of concept runs the agent on a narrow task with its limits in place, answering whether it does the job safely and stops where it should. If it does, production follows with supervision, logging and spend controls built in from the first sprint.
What is an AI agent?
Software that carries out a task by gathering inputs, calling your systems through defined tools, applying rules and escalating what it cannot decide - rather than just returning an answer for a person to act on.
Do you build fully autonomous agents?
As autonomous as the task safely allows. In a regulated operation many decisions stay human-in-the-loop: the agent proposes and a person approves. We set that line with you at design time.
How do you stop an agent doing something it should not?
Explicit permissioned tools, spend and rate limits, hard deterministic rules on the decisions that matter, a stop condition and an audit trail on every action. The agent gets only the reach its task needs.
Can an agent use our internal systems?
Yes, through tools we define as permissioned interfaces - read this record, call that API - never free-form access. Each tool is logged and constrained.
How do you keep an agent auditable and compliant?
Every action is logged with the input, the tool called and the outcome, so a decision can be reconstructed later. Models run inside your boundary where the use case requires it.
Who owns the agent afterwards?
You do - source, tools, prompts, guardrails and documentation, running in your cloud from the start.
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Have work an agent could carry?
Bring the task you want an agent to run and the systems it must reach. We will scope a proof of concept that proves it acts safely before any full build.