An agent is not delivered and forgotten. We operate your agents with continuous evals, step-level observability, drift detection, auditing and human control, so they keep doing the right thing when the data, the processes or the model change.
Models get new versions, data changes and processes change. An agent that worked well can degrade without anyone noticing.
AgentOps is the discipline of running agents as critical systems: measuring, detecting, auditing and correcting, with people at the right checkpoint.
Traces for every step (inputs, decisions, tools, costs, latency), built on open standards such as OpenTelemetry.
Custom test suites that run on every change and periodically in production.
Alerts when the agent's behavior, quality or cost drifts from its baseline.
Autonomy levels per action type, approvals and operational limits.
Offensive testing against instruction injection, data leakage and tool abuse, with our ethical hacking team.
An inventory of agents, owners, versions, permissions and evidence for internal audit, using frameworks such as the NIST AI RMF and ISO/IEC 42001 as a reference.
Which agents exist, who owns them, which versions are running and how they behave today.
Traces, quality and cost metrics, and a dashboard per agent.
Custom test suites and alerts on quality, drift and cost.
Agent-specific offensive testing and a remediation plan.
Reports on quality, incidents, costs and improvements applied.
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
Yes. We start with an inventory and a baseline, instrument whatever is missing and define evals before taking over operations.
It is the gradual deviation of its behavior from what is expected, caused by changes in the data, the processes or the model version. It is detected by comparing against a baseline and with periodic evals.
Every run: what the agent received, what it decided, which tools it used with which parameters and permissions, and what result it got.
Through live dashboards and a periodic report on quality, incidents, costs and improvements applied.
Yes, with agent-specific red teaming: instruction injection, tool abuse and data leakage.
Want to audit your agents' security? See ethical hacking →
We start with an inventory and a baseline: you will know what your agents do, what they cost and where the risks are.