We build agents that run complete processes inside your operation —not demos— together with the system around the model that makes them trustworthy: permissioned tools, independent verification, guardrails and traceability for every action.
Everyone has access to the same models today. The difference between an agent that impresses in a demo and one that runs a real process is the engineering around it: which tools it can use and with what permissions, who checks its work, what it remembers, what it is forbidden to do and whether anyone can reconstruct what it did.
That is called harness engineering, and it is what we specialize in.
An agent receives a request, queries the systems involved, runs the steps and delivers the result. It only escalates to a person when it should.
An orchestrator splits the work across specialized agents (research, execute, verify), with clear contracts between them.
Alert triage, incident correlation, deployment preparation and assisted runbooks. Always with human approval for irreversible actions.
They answer over your documents, policies and databases, respect the permissions of whoever asks and cite the source.
They extract data, validate it against business rules and resolve ambiguity. They verify before recording anything.
Assistants that prepare, draft and propose. The person decides and approves.
The model is the easy part. What makes an agent trustworthy is everything around it.
The model never touches a system directly: an intermediate layer validates, limits and logs every call.
Whoever checks is not whoever acts. A verifier approves every action with side effects before it runs.
The agent gets only what it needs, from permissioned sources, and forgets what it should forget.
Behavioral, data, tool and operational limits, defined before the first prompt is written.
Every step is traced: what it saw, what it decided, which tool it used and with what permission. Designed to catch drift before your users do.
Every task goes to the right model by cost, latency and data sensitivity, including private models.
Production errors become test cases. The agent improves on evidence, not intuition.
Process, systems involved, risks, autonomy level per action and measurable success criteria.
Least-privilege connectors or MCP servers, test environments and evaluation data.
Agent, verifier and eval suite. We iterate against evidence, not impressions.
Production with limited scope, human approval and full observability.
AgentOps: monitoring, drift, costs and new capabilities.
What we offer is what we use every day.
In Niveleads, our commercial platform, agents prospect, build campaigns, send automated emails and review how each lead engages. They also make AI voice calls, run technology watch, support growth and marketing, and read signals to give the team insights.
We build software with agents that write, test and review code, but we do not leave everything to AI. Deliveries go through an independent QA agent and verification standards defined by our team. Whoever builds is not whoever approves.
nivelics.com is designed to be read by agents and LLMs as well: a dynamically generated llms.txt, schema.org structured data and content written with LLMs in mind.
Illustrative scenarios of how we build. They do not describe clients or results.
An organization with many systems and an operations team flooded with alerts.
The agent correlates each alert with recent deployments and changes, queries logs and runbooks, classifies severity and proposes an action. Reversible actions pre-approved in the runbook, such as restarting a service, run once the verifier signs off, and the team is notified. For irreversible ones it prepares the plan and waits for human approval.
Harness: Read-only tools by default · verifier checks against the runbook · traces for every decision · evals built from past incidents
A process that crosses several systems and teams, with rework caused by inconsistent data.
An orchestrator receives the request. An integration agent validates data across the ERP, the document system and the database. An execution agent records the changes, and an independent verifier confirms consistency before closing. Exceptions reach a person with all the context already assembled.
Harness: One least-privilege MCP server per system · idempotent transactions with compensation · model routing · AgentOps dashboard
A chatbot talks. An agent acts: it queries systems, makes bounded decisions and completes tasks. That is why it needs permissions, verification and traceability that a chatbot does not.
No. We build an integration layer (APIs or MCP servers) on top of what you already have, including legacy systems. The agent works through that layer, never directly.
We design so errors are detectable and contained: an independent verifier, evals before every change, human approval for irreversible actions, operational limits and rollback. Every production error becomes a test case.
You decide. It can be processed in your cloud (Amazon Bedrock or Azure OpenAI, with regional deployments in the region you choose, no global or cross-region inference, and without the provider using your data to train its models) or on your own infrastructure with open-weight models.
It depends on the process, the integrations and the level of autonomy. In discovery we define scope, architecture and cost against measurable success criteria.
Your systems have no API? Start with integration →
In discovery we pin down the process, the risks and the autonomy level before writing a line of code.