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From one agent to an AI workforce.

AI Factory is manufacturing discipline, productized. Built on the Brain. Aimed by the Twin. Gated by evals. Governed on every call.

What is SphereIQ AI Factory?

AI Factory is where enterprise AI agents are designed, built, evaluated, and operated: a visual and pro-code agent builder, multi-agent workflow orchestration, model management, an evaluation harness with pre-production gates, and a template library of agents proven in real deployments.

A factory has stations. Here are the six.

  • Design

    Agent builder

    Visual builder with a pro-code escape hatch. Every agent declares its tools (MCP), knowledge scopes (Brain), memory (Engram), and policies (Governance).

  • Orchestrate

    Workflow builder

    Multi-agent workflows with human-in-the-loop steps, triggered by events, schedules, or an email hitting an inbox.

  • Test

    Evaluation harness

    Golden datasets, regression suites, LLM-as-judge scoring — and a Governance-enforced gate: no agent ships without passing its evals.

  • Route

    Model management

    Registry, A/B testing, fallback chains, and cost-aware routing that feeds AI Economics Intelligence™. Anthropic-primary, never model-locked.

  • Reuse

    Agent Template Library

    Start from agents proven in production, not blank canvases. Configure to your Brain and Twin; deploy in days.

  • Operate

    Run & observe

    Full tracing on every run, utilization dashboards in the Executive Console, and per-agent cost and ROI attribution.

Anatomy of an agent

Drafts stay drafts until they pass.

Draft agent

Visual builder with a pro-code escape hatch.

Metadata-filtered knowledge.

Anthropic-primary, never model-locked.

No agent ships without passing its evals.

The publish gate

An agent cannot be published until it passes its golden-dataset evals at or above its accuracy threshold.

Draft agentgolden datasets · accuracy ≥ thresholdPublishedBlocked publishes are recorded on the audit ledger like everything else.
Draft agentgolden datasets · accuracy ≥ thresholdPublished

Blocked publishes are recorded on the audit ledger like everything else.

Evals measure citation hit-rate against ground-truth answers, with latency tracked per run.

$1.2M/yr

Recurring savings from AI-run back-office workflows at an energy services firm

Days, not months

Template-to-production time on an existing Brain + Twin foundation

0

Agents in production without a passing eval suite. The gate is enforced, not encouraged.

Twelve agents that already have jobs.

Each template comes from a real Sphere deployment — pre-wired to Brain knowledge scopes, Twin process maps, and Governance policies for its role.

  • AP Agent

    invoice match & exception routing

  • Compliance Agent

    policy & evidence checks

  • Technician Knowledge Assistant

    field ops

  • Procurement Agent

    intake to PO

  • Revenue Assurance Agent

    leakage detection

  • HR Agent

    policy Q&A & onboarding

  • CFO Agent

    close support & variance

  • Content Pipeline

    governed marketing ops

  • Manufacturing Agent

    SOP & deviation support

Proven agent templates by department and vertical, cloned into draft agents your team then scopes and gates.

Get the Agent Template Catalog

Governed actions

When an agent's output triggers an action (email, Jira, Slack) —

  1. 01

    Validation

  2. 02

    Firewall

  3. 03

    Policy

    auto / requires approval / blocked

  4. 04

    Approvals queue

  5. 05

    Execution

Every step audited.

Asked before every Factory rollout.

Both, deliberately. Business teams use the visual builder; engineers drop to code with full SDK access. Either way, every agent declares the same tools, scopes, and policies — so governance doesn't depend on who built it.

Fallback chains route to alternate models or human queues, every run is traced end-to-end, and regression evals catch degradation before users do. Failure handling is designed in, because a workforce that fails silently is worse than none.

Factory works best on a Brain (knowledge) and Twin (targeting) foundation — that's why the chapters are ordered. But phased adoption is normal: many customers start with one template on one knowledge collection and expand from proof.

A workforce this capable needs exactly one thing above it.

Chapter 05: the reason your auditors, CISO, and CFO will say yes.