BlueBear platform

Deploy AI agents where your business already operates.

BlueBear governs AI agents inside your business: who they represent, which tools they can use, what needs approval, and the evidence each action leaves.

The BlueBear dashboard, showing the platform sidebar beside a grid of active agents with their runtimes, assigned models, and key counts.
The BlueBear sessions view, showing total sessions, tokens, tool calls, credits, and cost over daily usage charts.
BlueBear

How BlueBear handles the work

You get paid for the 20%. You rebuild the other 80% every job.

For agencies and consultancies that build automation for clients: connected systems and permissions set up once per client, approvals and a stop button the client can see, and a record of what each run did, per client. Can it act? Can it stop? Can it prove it? Answer yes for client two, three and ten without rebuilding.

Evidence: Per-client connections and permissions, visible approvals and stop, per-client run record

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Connected work

Agents, workflows, voice, MCP-connected tools, and human approvals operate together inside tenant and workspace boundaries rather than as separate disconnected pilots.

Evidence: Workspaces, agents, MCP connections, workflows, approvals in one boundary

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Governed tool access

Approved connections and permitted actions are evaluated at an execution boundary, so an agent acts with scoped authority instead of a reusable credential carried in model context.

Evidence: Assigned connections, permitted actions, policy decision, credential reference

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Controlled deployment

The same governed platform runs on managed infrastructure, in a customer-owned cloud account, or in a private environment, with operating responsibilities agreed per pattern.

Evidence: Managed, BYOC, and private deployment paths with explicit duties

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Inspectable outcomes

Sessions connect model usage, tool activity, retries, approvals, cost, and workflow results, so an operator can answer what happened and why after the fact.

Evidence: Session correlation, model routes, tool calls, approvals, retries, cost

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Cost you can attribute

Routing policy selects models per workload and Budget Manager attributes usage per tenant, workspace, and workflow, so spend maps to the work that caused it.

Evidence: Routing policy, per-tenant and per-workspace budgets, usage attribution

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From request to inspectable outcome

  1. Start with one workflow

    Name the business outcome, its owner, the systems involved, the data sensitivity, and what counts as an accepted result.

  2. Set the boundary

    Choose the tenant, workspace, identities, MCP tools, permitted actions, models, and budget the work may touch.

  3. Run with approvals

    Require human approval on irreversible, outbound, and budget-affecting steps while the rest runs and stays recorded.

  4. Operate from evidence

    Review routes, tool calls, approvals, failures, cost, and accepted outcomes, then tune policy from the record rather than from impressions.

Frequently asked questions

What does BlueBear do?
BlueBear runs AI agents in production. It provides governed workspaces, Model Context Protocol tool connections, human approval controls, session evidence, model routing, budget controls, and a choice of managed, customer-owned cloud, or private deployment.
Who is BlueBear for?
Primarily AI automation agencies, AI consultancies, managed service providers, and vertical SaaS providers delivering agent work for more than one client, plus enterprise platform teams standardising agent operations across business units.
When do teams outgrow running agents themselves?
Usually at the third or fourth engagement, when authentication, integrations, permissions, deployment, budgets, and support are being rebuilt per client. The cost stops being model spend and becomes the operating work around each agent.
Which systems can BlueBear connect to?
BlueBear connects to existing business systems, databases, and APIs through Model Context Protocol connections with scoped actions, so agents work around current systems of record rather than replacing them.
How do teams start with BlueBear?
Bring one production workflow with its systems, security boundary, expected volumes, and acceptance criteria to an architecture review. The deployment pattern and controls follow from what that workflow actually requires.