Nabhik AI agents
Agents that do the operational work
Autonomous workers that ingest, match, extract and explain under policy, with a human in the loop where it matters and a full record of every decision.
The suite
Six agents, one control plane
Each agent owns a class of repetitive work end to end. It reads the inputs, applies the rules you set, acts on what it is confident about, and escalates the rest with the context a person needs to decide quickly.
What makes these different
Built to run inside real operations
These are not chat wrappers. Each agent executes a defined workflow across your systems, under the same governance as the rest of the platform.
Autonomous workflow execution
An agent carries a task from trigger to outcome without waiting for a person at each step, following the policy you defined for it.
Cross-system orchestration
Agents read from and write to the systems already in use — ERPs, warehouses, object storage and SaaS APIs — instead of forcing work into a new tool.
Context-aware decisions
Decisions draw on the surrounding data, past outcomes and the rules in force, not a single prompt in isolation.
Exception handling and escalation
Anything outside the confidence threshold is routed to a named owner with the evidence attached, so nothing silently fails.
Human-in-the-loop controls
You decide which actions an agent may take on its own and which require sign-off, and you can change that boundary at any time.
Governance and auditability
Every action is tied to an identity, a policy decision and the inputs it saw, producing an audit trail without extra effort.
Lifecycle
From behaviour to continuous improvement
Every agent moves through the same three stages, so operating a new one feels familiar from day one.
- 01
Define behaviour
Set the rules, thresholds and escalation paths in plain language, and connect the systems the agent needs to read and write.
- 02
Deploy across workflows
Put the agent to work on live triggers, starting in a supervised mode where a person confirms actions before they take effect.
- 03
Monitor and improve
Watch accuracy, cost and escalation rates, tighten the rules where needed, and widen autonomy as confidence grows.
Questions
Frequently asked
- Do agents replace the people doing this work today?
- They remove the repetitive parts. People move to handling exceptions, setting policy and reviewing the decisions the agent is least sure about, which is where their judgement is most valuable.
- How much autonomy does an agent have on day one?
- As much or as little as you choose. Most teams begin with the agent proposing actions for confirmation, then widen autonomy for the cases it handles reliably once the evidence supports it.
- What happens when an agent is unsure?
- It stops and escalates. The task is routed to a named owner with the inputs, the rule that applied and the reason it could not proceed, so a person can decide quickly.
- Where do agents run?
- In the same deployment as the rest of VapusData OS — shared services, a dedicated instance, or on-premise and air-gapped — so data never has to leave your environment for an agent to act on it.
Put an agent on your slowest process
Pick the workflow that consumes the most manual hours and we will show you what an agent takes on and what stays with your team.