Scoped agents with real tools
Each agent gets the smallest set of tools that lets it finish its job — a queue, an API, a database read. Credentials are per-agent and per-action, never shared.
Service 02
AI agent implementation
A chatbot answers a question. An agent files the claim, reconciles the ledger, chases the supplier — then logs what it did and hands back the exception. We build the second kind, with boundaries written down before anything ships.
The problem
Most AI projects stall because nothing is allowed to happen.
The pilot answers questions impressively and then sits there. Nobody will connect it to a system that can change data, because nobody can say what it would do on a bad day — and they are right to hesitate. An agent with vague permissions is an incident with a start date.
The fix is not a better prompt. It is engineering: narrow scope, real tools with scoped credentials, a boundary written down and enforced, and an evaluation harness that proves behaviour on your own cases before anything reaches production.
What we install
Agents that take an action, log it, and hand back what they could not settle.
Multi-agent orchestration
Where one agent is not enough, work is split by role and handed between them explicitly. State lives in a store you can inspect, not in a conversation nobody can replay.
Evaluation harness
Before production, agents run against real historical cases with known outcomes. You see the pass rate, the failure modes, and the cost per run before committing.
Hard boundaries
Read-only where it should be read-only. No schema writes. No outbound network where the data is sensitive. Irreversible actions need a named human approval.
LLM ops
Versioned prompts and models, traced runs, cost and latency budgets, drift checks against the evaluation set. When a model changes underneath you, you find out from a dashboard.
Human in the loop
Exceptions route to a person with the context attached. Reviewed output, not improvisation — and the review feeds the next evaluation round.
The agent bay
Concrete, not theoretical.
- SENTINELDefense — Continuous control checks, log triage, anomaly escalation with context. Read-only on production; isolates a host only on named human approval.
- OPERATORExecution — Multi‑step back-office workflows across ERP, CRM, mail, and legacy screens. Scoped service accounts, per-action audit trail, no schema writes.
- ANALYSTJudgment — Document extraction, reconciliation, variance reporting, first-pass review. No outbound network; cites the source or withholds the answer.
- RUNNERThroughput — Queues, retries, scheduled batch, the jobs nobody wants to own. Idempotent tasks only; dead-letters after three failures and wakes a human.
What you get
Each step ends in something you can inspect.
- Week oneA shortlist of candidate workflows scored by volume, determinism, and blast radius — so the first agent is the one worth building.
- ThenOne agent in production on a narrow scope, with its evaluation results, boundary document, and kill switch.
- ThenWidened scope or a second agent, reusing the tool and evaluation scaffolding already built.
- OngoingLLM ops: traces, cost and latency budgets, drift checks, and a named escalation path.
Questions
Straight answers.
Which model do you use?
Whichever fits the task, the data residency requirement, and the budget — and we keep that swappable. Models change every few months; a system tied to one provider ages badly, so the agent's tools and evaluations stay independent of the model behind them.
Will it invent things?
That is what the boundaries and the evaluation harness are for. Agents that answer from documents cite the source or withhold the answer. Agents that act do so through typed tools with validation, so a malformed decision fails loudly instead of quietly writing nonsense.
Can it touch our production systems?
Only where you decide it should, through scoped service accounts, with every action logged. Most agents start read-only and earn write access one action at a time. Irreversible steps keep a human approval.
What about our data?
Data residency and retention are design inputs, not afterthoughts. Where the material is sensitive we run agents with no outbound network and keep processing inside your boundary.
Contact
Tell us what should execute.
Describe the work you want an agent to take off a person's desk. An engineer reads the brief, and we answer from Novi Sad within one business day.