Deploy and govern AI Agents on your infrastructure

  • Runs anywhere: Docker, Kubernetes, on-prem, air-gapped
  • Read-only by default. Approval required before any change.
  • AI Watchdog monitors the fleet continuously

Each agent runs in its own container.

data-sensei-overview

One agent cannot interfere with another. A shared dashboard shows every agent's status, cost, and activity in one place.
Agents talk through the channels your team already uses.
Two principles make it safe for production: configuration is malleable during setup but locks at promotion, and the
AI Watchdog runs as a separate agent that cannot be disabled by the agents it monitors.

Two starting points.

Every agent on the platform begins as one of two base templates. Each ships with secure chat,
a version-tracked workspace, and all platform guardrails wired in.
Low-Mileage

Headless Base Agent

The lighter option. Chats, reads files, calls APIs and command-line tools, reasons over data. No browser. The right default for work that lives in data and systems.

Best for:

Low-Mileage

Browser Base Agent

Everything the Headless agent does, plus a controlled browser inside the container. Researches the open web, navigates web apps, fills forms, and verifies its work visually with screenshots.

Best for:

//Benefits

From setup to production.

Agents follow a deliberate lifecycle. This is the mechanism that
 prevents instruction injection from changing production behaviour.

Stage What happens
Setup Agent is given an identity, channel, workspace, and role. Configuration is malleable.
Testing Agent runs against real data. Outputs reviewed. Guardrails adjusted.
Promotion Configuration is locked. Captured in a snapshot. Production behaviour is now immutable.
Production Agent runs continuously. AI Watchdog monitors. Approval required for changes.
Rollback Restore to any earlier snapshot. AI Watchdog consulted before the restore proceeds.

Continuous security for the agent fleet.

A dedicated monitoring agent that runs alongside the others. It is the only agent that cannot be disabled by the agents it monitors.

What it watches for:

  • Prompt injection attempts
  • Data exfiltration patterns
  • Configuration tampering
  • Leaked credentials (reported with secrets masked)
  • One prioritised alert per check: Critical, High, Medium, or Low
  • Consulted before any backup restore
data-sensei-overview

// WHY CHOOSE bitsIO?

Protect what the agent learned.

Agents evolve. They refine instructions, build skills, accumulate context about your environment. The platform protects this work.
This protects the agent's own configuration and learned state. Not your data or infrastructure.

End-to-End_Splunk

Configuration snapshots

Point-in-time backups of identity, skills, and rules. Restore is one checkpoint selection.

247-Monitoring

Learned-state continuity

Optional backup of memory and conversation context. Recovered agents keep what they learned.

Customized-Solutions

Guarded restores

Operator confirmation required. AI Watchdog consulted before any restore.

Cost-Effective

Secret scrubbing

Automatic scan aborts backup if credentials or tokens would be stored

Sized to how you use it.

Cloud-routed inference needs almost nothing. Always-on local inference wants dedicated GPUs.

Pattern Agents Recommended Hardware
Light, cloud-routed 1–3 CPU only or shared GPU
Steady, team-wide 4–10 Single GPU: L4 or A10
Heavy, always-on 10+ Multi-GPU: A100 or H100
Edge or OT Varies Unified Edge appliance

Unified Edge for OT

A ruggedised on-prem appliance that runs agents next to the production line. Air-gap capable, optional GPU. Built for manufacturing, energy, and critical infrastructure.

// Insights

Insights & Resources

Dive into our extensive library of resources tailored to enhance your experience with Splunk and other leading technologies. Keep up with the latest industry trends, best practices, and expert insights to fuel innovation and help you reach your goals.

// bitsIO’s SOLUTIONS & SERVICES EXPLAINED

Frequently Asked Questions

How is an agent promoted to production?

An administrator approves it explicitly. After promotion, the configuration locks. Any change starts a new deployment cycle. This is by design.

What language models does it use?

Cloud-hosted or local. You choose. A model gateway handles routing, costcaps, and failover. Air-gapped deployments use local inference on GPU hardware

What data platforms does it connect to?

Splunk, Elastic, cloud data lakes (S3, ADLS, GCS), Grafana, ServiceNow, and any system with an accessible API. The platform is data-source-neutral; integration adapters are configured per agent. AI Watchdog telemetry can be streamed to any data platform you operate.

How is this different from other agent frameworks?

Most are built for developers prototyping. This is built for production: immutable configuration, dedicated security monitoring, audit-grade logging, backup and rollback, and deployment on infrastructure you control.

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