Responsible AI Framework
Building trustworthy agentic AI, by design — governance and controls for autonomous agents that plan, decide, and act.
Prepared by the Responsible AI Council, Kroolo Labs
Plan → Decide
→ Act
→ Act
Human override
Audit trail
Kill switch per agent
Action allow-lists
Purpose & Scope
Autonomy only works if every action can be trusted.
Kroolo builds agentic AI — systems that don't just generate content but plan, decide, and act across work management (Kroolo Work) and enterprise search and intelligence (Kroolo Search), including for regulated sectors such as financial services and insurance.
Agentic autonomy raises the stakes beyond generative AI: small errors can chain into material business, compliance, or safety impact. This framework makes that autonomy bounded, observable, and auditable — at every step.
Every System
All models, agents, and orchestration layers Kroolo builds, fine-tunes, or integrates via third parties
Every Environment
Internal tooling, customer-facing products, pilots, and proofs of concept
Every Lifecycle Stage
Design, data sourcing, development, evaluation, deployment, monitoring, decommissioning
Everyone Involved
Kroolo personnel, contractors, and third-party partners across the AI supply chain
Guiding Principles
The principles that govern every agent we build.
Human Agency & Oversight
Humans retain meaningful control over consequential decisions, with clear override paths.
Fairness & Non-Discrimination
Systems tested for disparate impact before and after deployment.
Transparency & Explainability
Users can understand what an agent did, and why, in plain language.
Accountability
Every action has a named owner, an audit trail, and a chain of responsibility.
Privacy & Data Stewardship
Minimization, purpose limitation, and lawful basis govern all data use.
Security & Robustness
Systems resist prompt injection, poisoning, and extraction — and fail safely.
Reliability & Safety
Explicit, enforced limits on autonomous action versus what needs approval.
Standards Accountability
Controls map to recognized external standards, independently verifiable.
Continuous Improvement
The framework is versioned, audited, and revised on a fixed cadence.
Risk Tiering
Autonomy is additive, never assumed.
Every agent capability is classified before release — graded on autonomy of action, not just output — aligned to the EU AI Act's risk-based approach.
0
Tier 0
Minimal
Read-only, informational, fully reversible
ExamplesSearch & retrieval, summarization
1
Tier 1
Limited
Drafts or suggests; human approves before any action
ExamplesDraft task, draft email, suggested priority
2
Tier 2
Elevated
Executes reversible actions within defined boundaries
ExamplesAuto-assigning tasks, scheduling, status updates
3
Tier 3
High
Acts with financial, legal, contractual, or regulated-data impact
ExamplesWorkflow actions, compliance-feeding extraction
Tier 4
Prohibited
Fully autonomous, irreversible impact, no human checkpoint
ExamplesNot built by Kroolo under any circumstance
Lifecycle Controls
Controls run the full life of an agent.
Design
Risk tier set upfront; model/agent card documents intended & out-of-scope use
Data
Provenance & consent documented; tenant isolation enforced by default
Develop & Evaluate
Bias testing, red-teaming, and full reasoning-trace logging before release
Deploy
Staged rollout with guardrails, action allow-lists, and a kill switch per agent
Monitor
Continuous drift & bias monitoring; defined incident SLAs
Decommission
Deprecation plan with data retention/deletion and customer notice
Customer Commitments
What every Kroolo customer can rely on.
Disclosure
Customers are told when they're interacting with an AI agent, and exactly what actions it's authorized to take on their behalf.
Configurability
Every tenant can set its own human-in-the-loop threshold — more conservative than Kroolo's defaults is always available.
Auditability
Every agent action logs a full reasoning trace, exportable for the customer's own compliance and audit needs.
Data Boundaries
Tenant data is never used to train models shared across customers without explicit, revocable opt-in.
Right to Explanation
Any customer or end-user can request an explanation of a specific agent decision or action.
Metrics
What the Responsible AI Council tracks.
1
Bias/fairness audit pass rate, by release & product line
2
% of Tier 2+ actions with a completed audit trail
3
Red-team finding severity & time-to-remediation
4
Hallucination / error rate on production workflows
5
Incident count, severity, and mean time to resolution
6
% of customers on human-in-the-loop configurations vs. defaults
7
Time since last independent audit / certification renewal
Adoption
A reference standard, not a proprietary constraint.
1
Request the underlying evaluation artifacts — model/agent cards, audit reports, mapping documents
2
Adapt the risk-tiering model to your own internal risk appetite
3
Propose amendments as part of contractual AI terms where your regulatory obligations require more
Responsible AI, for an agentic AI company, is not a document — it's the set of guardrails that make autonomy safe to grant.
Kroolo Labs · Next review within 12 months or upon material regulatory change
Want to see the evidence?
Request the underlying evaluation artifacts, or talk to our team about your own risk appetite.
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