Service : build
Agents that finish the job.
AI agents are software that can read a situation, decide what to do, use your tools and complete multi-step work with supervision. Novistu builds production agents on LangGraph and similar frameworks, with evaluation, guardrails and audit trails, so they are dependable enough to put in front of real customers and real operations.
The mechanism
Not chatbots that answer questions.
- Request
- Plan
- Tools
- Verify
- Act
The problem
What this fixes
Single prompts cannot handle real work
Real tasks span multiple steps, systems and decisions. A single prompt to a language model handles none of that reliably. Agents structure the work: plan, act, verify, escalate.
Demos are easy, production is not
An agent that works eight times out of ten is a liability in production. The difference is engineering: evaluation, guardrails, retries, audit trails and clear escalation paths.
Agents must respect your systems
An agent that cannot check your CRM, read your documents or trigger your workflow engine is just a text generator. The value comes from tight, permissioned integration.
How we build it
Principles before code.
01
Start from the task, not the model
We define the task, its inputs, its definition of done and its failure modes before choosing any model. The model is a component, not the product.
02
Design the loop
Plan, act, verify, escalate. Each agent gets tools with scoped permissions, memory of what matters, and a supervisor pattern so a wrong step costs one retry, not a cascade.
03
Evaluate before launch
We build test sets from your real cases and measure accuracy, cost and latency. Agents ship with regression suites so improvements never silently break what worked.
04
Operate with visibility
Every run is traceable: what the agent saw, decided and did. You get dashboards and alerts, and every consequential action has a human approval gate until proven.
Delivery
From first call to running system.
- 01
Define
Pick the highest-value task an agent could own. Write down what done looks like and where the agent must stop and ask a human.
1 week - 02
Prototype
Build the first loop against real data and tools. Measure it. If it cannot beat the human baseline on the test set, we stop early and say so.
2 to 3 weeks - 03
Harden
Guardrails, retries, escalation paths, cost controls and the evaluation suite. Chaos-test with edge cases before anything touches production.
2 to 4 weeks - 04
Deploy and supervise
Gradual rollout with human review queues. We tune prompts, retrieval and tool designs against live traces until quality is stable.
Ongoing
What you get
- Agent architecture: roles, tools, memory design and supervision model
- Working agents in production with scoped access to your systems
- Evaluation suite with your real test cases and quality thresholds
- Guardrails: input validation, output policies, rate and cost limits
- Run traces and audit logging for every agent action
- Runbook: what the agent handles, what it escalates, who is on call
Proof
Systems like this, in production.
Questions
Asked before every build.
Practical examples we have shipped: reading inbound enquiries and preparing quotes, screening candidates and scheduling interviews, triaging support tickets and drafting responses, extracting data from documents into your systems, and monitoring inboxes for actions that need follow-up. The pattern is always: multi-step work with clear rules about when to ask a human.
Whatever the task and your constraints favour: OpenAI and Anthropic models for most language work, open-weight models where data residency or cost requires it. We are vendor-neutral and design the agent layer so models can be swapped as they improve.
Four layers: tools are permission-scoped so agents can only do what their role allows, consequential actions require human approval, guardrails validate inputs and outputs, and every run is traced so nothing happens silently. Agents earn autonomy gradually as their evaluation results prove it.
A focused single-task agent typically runs a fixed-scope project of a few weeks. Multi-agent systems with deep integrations are larger. After a scoping call we give you a written range, and we are straightforward if an agent is not the right tool for your problem.
No, and that is not the goal. Agents take the repetitive middle of a process: reading, checking, drafting, updating systems. Your team handles judgment, relationships and exceptions. The companies that get this right redeploy people to the work that actually needs them.
Tell us what is eating your team's hours.
A short brief, answered within one working day. The first call is free, and if AI is not the right answer, we will say so on that call.
First call free : honest about fit : no obligation