GENELLIPSE FOR ENTERPRISE
Cortex AI product brief
Build and operate enterprise AI agents with policy controls, human oversight, and a traceable record of their actions.
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Business purpose
Cortex AI OS brings agent creation, workflow automation, governance, and monitoring into one runtime. Connect your models, knowledge, and business systems while keeping permissions, budgets, and approvals attached to the work.
Areas to evaluate
Confirm the capabilities and configuration appropriate to your requirements.
Build agents and workflows
Combine skills, business knowledge, and tools in Agent Studio to create agents around the way your team works.
Set the boundaries
Apply identity, budget, and policy controls. Route consequential actions through human review.
Understand each outcome
Monitor runs, evaluate quality, and inspect the Trust Ledger to follow an action from its context to its result.
Plan an MVP within 6 weeks
Genellipse plans a focused MVP within 6 weeks. Agree the outcome, interfaces, access, client reviewers, and acceptance criteria at the start. Use the delivered evidence to decide the next stage.
Your starting point
A named process owner, representative examples, current measures, and the constraints the solution must meet.
Evaluation output
An agreed workflow, tested configuration, findings, operational responsibilities, and a recommendation for the next phase.
A workflow to evaluate
Evaluate a bounded agent workflow that gathers approved evidence, drafts an interpretation, and routes a proposed action to the right authority. Assess a single agent first, then coordinated agents or packaged workflows where the handoffs create business value.
Define authority before autonomy
Identify permitted sources, tools, users, and actions. Separate read access, recommendation, and execution; specify which actions always require review.
Connect agents and existing systems
Assess approved APIs or MCP tools and the treatment of externally supplied agents. Agree contracts, identity propagation, budgets, tool scopes, and failure handling.
Package and operate a useful workflow
Test the complete journey across agents and systems, including interruptions. Define the review route and acceptance evidence before packaging a solution for wider organizational use.
Security and accountable operation
Govern each tool and data boundary, not merely the language model. Review credentials, scoped permissions, source freshness, prompt-injection scenarios, and logging. Confirm which governance capabilities apply to the proposed platform configuration and brought-in agents.
Integration and operating boundaries
Document API and MCP contracts, authorization, timeout behavior, and idempotency for actions. Tool discovery alone must not grant permission to execute.
Exceptions and professional review
Route insufficient evidence, conflicting instructions, unauthorized requests, and unavailable dependencies to a controlled failure or human review. Keep financial and regulated decisions within agreed authority.
Measure value without hiding the work
Measure accepted task completion, serious errors, review time, escalations, repeated tool calls, and cost per completed outcome. More agents or more generated tokens are not measures of business value.
What to bring to an evaluation
Bring representative tasks, difficult examples, approved sources, prohibited actions, tool documentation, domain reviewers, and a current operating baseline.
What a useful acceptance pack contains
Include an authority map, evaluation set, configuration versions, observed outcomes, exception evidence, operational ownership, and conditions for expanding autonomy.
Planning material. Product fit, available configurations, commercial terms, and service commitments are confirmed for the engagement.
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