Features

One governed platform instead of fragmented AI sprawl

Chat-first for teams. Dashboard-first for IT. Policy and audit for everyone.

Tool Registry

A central, owner-managed registry of every external system agents can access. Tool owners define what functions are exposed, what approvals are required, which models are allowed, and how usage is billed. Every tool connection goes through the registry, or it doesn't go through at all.

Skill Library

Reusable behavior packages that define how agents know to do something well. Inbox triage, performance review preparation, department-specific playbooks — versioned, published, and rollback-capable across teams. Like an internal package registry for agent behavior.

Policy Engine

Define who can access what, which models are allowed where, where data can and cannot flow, and what actions require human approval. Hard guardrails block violations pre-flight. Soft guardrails flag anomalies post-hoc. Policy is the architecture, not the patch.

Execution Logs & Replay

Full traceability for every agent action: the prompt, the intent, every tool call, every approval, every output, every failure. Immutable and tamper-evident. Compliance teams can replay any session end-to-end.

Human-in-the-Loop

Approval workflows for sensitive operations — CRM writes, financial transactions, data exports, external communications. Escalation paths for edge cases. Kill switches for when the agent needs to stop. Humans stay in control of the actions that matter.

BYO-LLM

Model-agnostic by design. Bring your own LLM contracts — ChatGPT, Claude, Gemini, Ollama, Mistral. The platform tracks usage and cost by team, tool, department, and model. You're never locked into a single provider.

Honeypot Tools

Intrusion detection for AI agents. Dummy tools that look like they expose real secrets but return fake data and trigger immediate security alerts. Catches prompt injection, compromised agents, and unauthorized exfiltration. Zero false positives.

Identity Layer

Every action traces to an identity: human, individual agent, team agent, or service agent. A four-level cascade determines persona, tone, guardrails, and topic boundaries. Context is scoped, identity is verified, accountability is continuous.

Environment Separation

Dev, staging, and production environments for agents, tools, and policies. Test new configurations, skill versions, and policy changes in staging before promoting to production. This is how enterprise software works.
Tools vs Skills

Two different things. One clean separation.

Most AI platforms treat tools and skills as the same thing. They're not. Understanding the difference — and governing them separately — is what makes enterprise agent adoption actually work.

Tools are what agents can touch. Gmail, Salesforce, Jira, internal databases, calendar, Slack — every external system an agent connects to. Tools have owners, scopes, permissions, approval requirements, and model constraints. When you govern tools, you govern access.

Skills are how agents know to do something well. Inbox triage, performance review preparation, follow-up drafting, department-specific playbooks — reusable behavior packages that define competence. Skills are versioned, published, and rolled out across teams. When you govern skills, you govern behavior.

Why this separation matters for enterprises

  • Govern access separately from behavior. The CRM team controls who can touch Salesforce. The HR team controls how agents handle employee data. These are different concerns that require different governance.
  • Reuse skills across many tools. An inbox triage skill works for Gmail, Outlook, or any email provider. The behavior is reusable; the tool access is specific.
  • Let tool owners define their own constraints. The Salesforce admin sets approval requirements for CRM writes. The security team sets PII redaction policies. Skills work within these constraints.
  • Give IT a single control plane for both. The tool registry manages access. The skill library manages behavior. One dashboard. Two distinct governance models.

Tools without skills are open pipes with no competence. Skills without tools are capabilities with no access. You need both — and you need to govern them separately.

LLM-First Discovery

The LLM discovers. The app codifies. The system accelerates.

Reality is messy — you can't pre-build for every provider, every API shape, every auth flow. The LLM figures it out first. Then you pave what works into fast, deterministic, approved code.

When a user asks "How's my inbox?", the agent doesn't need a pre-built Gmail integration. The LLM discovers the provider, finds the right token, calls the right API, and delivers the result. Cost: a few cents. Latency: a couple seconds. It works.

But that's not the endgame. The endgame is speed, cost efficiency, and governance. After the LLM discovers the pattern 50 times in audit logs, someone builds an app for it. IT approves it through the pipeline. Now inbox queries hit the app directly: deterministic, zero tokens, 50 milliseconds.

Week 1

LLM discovers

Agent handles "How's my inbox?" — discovers Gmail, finds the token, calls the API. Cost: $0.003. Latency: 2s.

Week 4

Pattern recognized

50 inbox checks visible in audit logs. Clear signal that this is a repeated workflow.

Week 5

App created

get_emails() is built, IT-approved, and deployed to the app registry. Deterministic. No LLM needed.

Week 6

Router optimizes

Inbox queries go directly to the app. Zero tokens. 50ms. The LLM created the roadmap.

Two execution paths

Apps (deterministic)Agents (non-deterministic)
OriginBuilt after recognizing LLM patternsLLM discovers on the fly
RunsApproved code — Python/TS functionsLLM prompt + generic tool calls
ApprovalIT reviews and deploys via pipelineConfigured by agent owner
CostCompute only — no tokensLLM tokens per invocation
SpeedMillisecondsSeconds
AuthKnows exactly which token to useDiscovers provider, token, and API shape

From experimentation to enterprise-grade in one governed layer

Govern what agents can touch. Trust what they do. See your enterprise AI adoption done right — with visibility, control, and audit built into every action.

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