One agent. In the app, and in yours.
Anima works alongside you inside the platform: an agent with full context on every role, task, tool and process. And the same agent and graph are open over MCP, an API and a CLI, so your own agents and tooling can drive the work too.
An agent that already knows your org. No uploading context, no re-explaining your structure: ask a real question and Anima reasons over your live graph, with every answer grounded in task-level evidence.
Which roles in my Customer Experience org have the most task overlap? Show me hybrid role opportunities where AI compression could combine responsibilities.
Customer Experience: Hybrid Role Opportunities
- Combines account management, technical support and onboarding delivery
- AI handles ticket routing, health scoring, playbook execution and routine check-ins
- Capacity impact: 3 roles become 2 enhanced roles per pod
An expert on what every role is becoming.
The same in-app agent, scoped to each person. Every employee gets a role-specific guide grounded in their actual task map, not generic career advice.
I'm a Customer Success Manager and my company is pushing us to become 'Strategic Advisors'. What does that actually mean? What skills do I need, and how do I develop them while AI handles my old playbook work?
CSM to Strategic Advisor: Your Development Path
- Month 1: master your AI copilot, automate all health scoring and playbook triggers
- Months 2 to 3: shadow 3 executive QBRs; practice hypothesis-driven discovery
- Month 4: lead one strategic business review, not a product review, solo
The same agent, in your tools.
Connect Claude Code, cowork, or any MCP client to your graph, or script it from the CLI. Your governance boundaries stay intact: agents propose, your people decide.
Make the graph your AI infrastructure.
Expose the Human Layer Graph over MCP, a REST API, or the CLI, and every agent you build can query a rich, structured, analysis-backed model of how your organisation actually works.
Claude Code
Give your coding agent full context on every role, system and dependency as it works.
Claude cowork
Let cowork agents reason over your live org: plan headcount, model changes, draft JDs.
Any MCP client
Cursor, Cline, your own internal agents: anything that speaks MCP can query the graph.
One prompt. Any document.
The agent doesn't just answer questions, it writes the deliverables. Living JDs, executive reports, transition plans, board briefs, capacity ledgers: every document is generated from your graph, grounded in task-level evidence, and stays current as the analysis moves.
Generate a living job description for a hybrid GTM Engineer role: combine Sales Development Rep with Software Engineer at 65% AI automation.
GTM Engineer
- Design and maintain the complete lead lifecycle from first touch to closed-won
- Build custom integrations connecting CRM, enrichment APIs and outbound platforms
- Deploy AI-powered prospecting workflows: signal-based outbound, intent-triggered sequences
- Create real-time dashboards tracking pipeline velocity, conversion and attribution
A deterministic measurement and execution layer.
Every action is logged, traceable to task-level evidence, and reproducible. No black boxes, so you can trust what the agents did and prove what changed.
Put your agents to work on your workforce.
See how the agent interface drives Discovery, Transform and Traction on your graph.