Business problems, agentic delivery
CogniCube does not start with chatbots. It starts with a use case — then discovers, decides, orchestrates agents, and loops on results.
Business Use Case → Agents
Every engagement starts as a business problem. Attach requirements, Explore against the enterprise graph, Proceed, then run a lean agent lineup — not a fixed mega-team.
Example scenarios
- Classify intent: greenfield, enhance, or test-only
- Propose next steps and systems to touch from the graph
- Size agents to the work (QA-only vs full SDLC)
- Human Proceed before any specialist agent runs
Software / SDLC Delivery
Shift the SDLC left: discover dependencies before build, continuously implement and test with agents, and measure time to market — not story-point theater.
Example scenarios
- New feature with product, architecture, eng, and QA agents
- Regression validation without a full developer cast
- Impact and blast radius before change lands
- Closed loop: results feed Explore and Decide again
Financial Institutions
Compose SDLC agents with financial-domain specialists — payments, core ledger, lending, risk, KYC/AML, and controls — grounded in your bank’s system graph.
Example scenarios
- Payments settlement change with risk and QA in the loop
- Ledger feature validation against known platforms
- Compliance review with financial control agents
- Owners and blast radius from the enterprise graph
Governed Orchestration
Serial and parallel agent graphs, structured handoffs, and human gates. Chat over documents still helps — but delivery is an operating loop, not a Q&A thread.
Example scenarios
- Handoff payloads: findings, artifacts, graph citations
- Parallel research and validation with joins
- Gates for approve, edit, reject, or rerun
- Agent catalog: create, enable, and compose personas
Ready to run your next use case?
See Explore → Decide → Run → Results on your enterprise context.