AI agents development & integration

An AI agent carries out a task end to end: it gathers inputs, calls your systems through defined tools, applies rules and escalates what it cannot decide, rather than returning an answer for a person to act on. In a bank or fintech that means an agent can run work like onboarding, reconciliation or case handling under supervision, not simply draft a reply. WislaCode builds agents for regulated financial operations, where the reach of each agent is set to the task and no wider.

Look for guardrails before autonomy: explicit permissioned tools, hard deterministic rules on the decisions that matter, spend and rate limits, a stop condition, and a full audit trail on every action. Ask where the models run and who holds the data, whether you own the source, tools, prompts and guardrails, and whether swapping model or vendor is a configuration change rather than a rebuild. In regulated fintech those answers decide whether an agent is safe to put into production, which is the standard we build to.

Building an agent that can act inside a regulated bank is mostly the work around the model: the permissioned tools, the deterministic guardrails, the audit trail, and the integration with systems you already run. An internal team can own this, and a partner who has built it before shortens the path on the parts that are easy to get wrong and expensive to retrofit. We prove it on a fixed-price, time-boxed proof of concept on one narrow task first, so the decision to continue rests on evidence rather than a large upfront commitment.

Yes, when the guardrails are designed before the autonomy. We keep the decisions that matter under hard deterministic rules, log every action with its input, the tool called and the outcome so an onboarding, reconciliation or case-handling decision can be reconstructed later, and run models inside your own boundary where the use case requires it. Because we build regulated banking and fintech software, the audit trail and the compliance boundary are part of the design from the first sprint.

An agent reaches your systems only through tools we define as permissioned interfaces (read this record, call that API, file that ticket), never free-form access, and each tool is logged, rate-limited and spend-limited. Where capability allows we run self-hosted or private-endpoint models so your data stays on your own infrastructure, with contractual limits on any third-party endpoint. It is only as autonomous as the task safely allows: hard deterministic rules constrain what the model may decide, and where a decision must stay with a person in a regulated bank or fintech the agent proposes and a human approves.

Yes. Much of banking runs on older systems where a clean API rarely exists, so we wire the agent in through the interfaces you actually have and constrain each one as a defined, permissioned tool. Whether the agent supports a core platform or a mobile banking front end, it reaches those systems only through tools we log and limit, so integrating it does not widen its reach.

We build for production from the first sprint, with logging, spend controls and a supervision path in place rather than added later. Multi-agent workflows are co-ordinated through a controller, each agent with a narrow job, a clear hand-off and a stop condition, which keeps behaviour predictable as volume grows. WislaCode proves the guardrails on a narrow task first, then moves the same controlled design into a regulated financial workload at production scale.

You do: the source, tools, prompts, guardrails and documentation, running in your own cloud from the start and handed over with a runbook. We keep a clean interface between your product and any model or vendor, with a fallback and a swap path, so changing provider later is a configuration change rather than a rebuild. For a bank or fintech, that means no lock-in, to us or to a single model provider.