The ATLAS Framework
Agentic Trust, Logic & Assurance Standard
Tuning Engines created ATLAS as an open, vendor-neutral guidance framework to advance our mission: a safe and reliable way to harness the exponential capabilities of AI workers — software agents and embodied robots alike.
ATLAS is to the agentic world what SOC was to the cloud
Exponential agent capability needs a matching standard of trust.
Modern AI workers — LLM copilots, autonomous agents, and robotic systems — perceive, decide, and act on their own. Their adoption hinges on trust. Without a shared definition of reliability, security, transparency, fairness, and accountability, every team invents its own bar and none of them are comparable.
Tuning Engines authored ATLAS as guidance the whole ecosystem can use — open, not-for-profit, and vendor-neutral. It is the assurance vocabulary behind the compliance, security, observability, and intelligence controls we operate in our own platform.
Neglecting any single dimension of trustworthiness — safety, security, fairness, oversight — increases the risk of errors, misuse, or harm. ATLAS makes every dimension explicit and assessable.
A common language and structured criteria.
Open, not-for-profit
A community-usable standard for the agentic world, published for the common good.
Nine trust domains
A comprehensive structure covering the full lifecycle of agentic and embodied AI systems.
Two maturity levels
Level 1 (Baseline) and Level 2 (Advanced) criteria per domain, so teams can assess and improve.
Practical, vendor-neutral
Best practices for safe, responsible, compliant deployment — independent of any platform.
A path to attestation
A roadmap toward formal assessment and certification, today in the guidance phase.
Built for agents and robots
Core domains for software agents, plus the ATLAS-R extension for embodied systems.
Nine trust domains.
Each domain carries Level 1 (Baseline) and Level 2 (Advanced) criteria. Teams assess every domain, record evidence, and track maturity over time.
Governance & Accountability
Leadership, policies, and processes for overseeing AI agents — clear ownership and accountability for agent actions and outcomes.
Defined roles, documented policies and ethics guidelines, compliance checks, escalation path, human sign-off on critical actions.
Dedicated AI governance board, continuous risk management and audits, transparent disclosures, comprehensive training, robust record-keeping.
Safety & Reliability
Operating without causing harm and performing consistently as intended — functional safety for software, physical safety for robots.
Basic testing and validation, graceful failure modes, safety constraints, reliability metrics monitored, user feedback loop.
Stress, adversarial and simulation testing, continuous monitoring and redundancy, recovery procedures, fault injection and formal verification.
Security & Privacy
Protecting the agent from unauthorized access, misuse, or breach, and handling sensitive data correctly.
Least-privilege access control, vulnerability management and patching, privacy compliance, secure channels, opt-out controls.
Threat modeling and defense in depth, intrusion and anomaly detection, secure supply chain, privacy-enhancing technologies, red-teaming.
Transparency & Explainability
Stakeholders can see how the agent works and understand why it acted — visibility plus intelligible reasons.
Documented capabilities and limitations, activity logging, simple explanations for key decisions, clear error communication, informed consent.
Explainable decision logic, end-to-end traceable audit trails, stakeholder-tailored explanations, proactive disclosure of model and data provenance.
Fairness & Ethics
Equity, impartiality, and alignment with human rights and societal values across every population the agent touches.
Bias awareness in data and outputs, representative testing, non-discrimination policy, channel for reporting harm.
Quantified fairness metrics with thresholds, ongoing bias monitoring and mitigation, ethics review of high-impact use cases, stakeholder consultation.
Autonomy & Oversight
Balancing independent decision-making with human supervision — agents stay inside intended boundaries and humans can intervene.
Defined autonomy boundaries, human-in-the-loop for high-impact actions, kill switch or pause control, action logging for review.
Graduated autonomy tied to demonstrated performance, real-time intervention tooling, delegated authority policies, oversight effectiveness measured.
Logic & Reasoning Integrity
Soundness and verifiability of internal reasoning and knowledge — guarding against hallucination, flawed inference, and bad plans.
Grounding of factual claims, validation of tool and plan outputs, guardrails against unsupported assertions, error rate tracking.
Structured reasoning verification, evaluation suites for planning quality, provenance for retrieved knowledge, regression testing on reasoning failures.
Performance & Efficiency
Task effectiveness and resource efficiency — the agent achieves its goal at a defensible cost per outcome.
Defined success criteria, baseline task accuracy and latency tracked, cost per task visible.
Continuous outcome evaluation, routing and optimization against cost and quality targets, capacity planning, efficiency regression alerts.
Collaboration & Interaction
Quality of agent-to-agent and agent-to-human interaction, including multi-agent coordination.
Clear interaction protocols, handoff rules between agents and humans, conflict and deadlock handling.
Verified multi-agent coordination behavior, shared context and memory governance, interaction quality measured with human feedback.
Extensions for embodied agents.
Robots act in the physical world, so ATLAS adds two specialized domains on top of the core nine.
Embodied Safety
Physical safety for humans and the environment: mechanical safety standards (e.g. ISO 10218), collision avoidance, emergency stops, sensor and actuator reliability, and safe-by-design mechanics. Augments Safety & Reliability.
Human-Robot Interaction (HRI)
The quality and ethics of physical interaction: signaling robot intent, adhering to social norms, comfortable instruction and correction, respecting human agency, and avoiding misuse of trust in anthropomorphic systems. Extends Transparency, Fairness, and Oversight into the physical realm.
Applying ATLAS.
Per-domain assessment
Evaluate whether Level 1 and Level 2 criteria are met in each domain. Assign Level 0 (not met), Level 1, or Level 2.
Qualitative rating
Document the evidence and rationale behind each assigned level. This is the core of the assessment.
Optional scoring
Assign points per domain for an overall maturity score. Weighting can be adjusted for context, if disclosed.
Continuous improvement
Re-assess periodically — annually, or after major changes — to demonstrate commitment and track trend lines.
From framework to enforcement.
ATLAS defines what trustworthy agentic AI looks like. Tuning Engines is the control plane that operationalizes it — policy-as-code, runtime guardrails, traces, and outcome and cost accountability for every AI worker you employ.