Your AI agents decide what to do. But who is responsible for their actions?
Alethesis AI ensures every AI agent decision is transparent, risk-assessed, controlled, and routed to the accountable human - at runtime, before anything executes.
Seamlessly integrates with the global AI ecosystem
Four accountability gaps every agentic AI deployment hits.
Most organizations enforce what their AI agents can do. Almost none govern who is responsible when an agent acts. As agents move from suggesting to executing, three structural gaps impact your business.
The wrong hands on the wheel.
The team that built the agent isn't the team accountable for its decisions. When an action goes wrong, the technical owner can explain how the agent works but can't answer for what it did. The person who should be answering wasn't part of the decision.
The agent drifts past its limits.
Agents change after they're deployed. They accumulate memory, learn from tool use, and develop strategies no one designed. The shifts look normal day to day. By the time anyone notices, the agent is operating outside the boundaries it was originally approved for and often outside what regulation allows.
The human's contribution disappears.
When a person does step in to review or correct an agent, there's no record of what they prevented. Oversight starts to look like overhead. Leadership cuts what it can't measure and the reviewers who quietly stopped the most damage are the first to go.
Oversight is all-or-nothing.
Today's controls treat every agent action the same: stop the workflow, wait for sign-off, resume. Low-risk and high-risk actions get the same friction, so teams either pull the agent or wave everything through. Neither is real oversight.
Infrastructure for human-AI interaction.
Alethesis AI sits between your agent's reasoning and its execution. Every planned action is intercepted, classified, scored and routed to the right authority level, before anything irreversible happens.
Fill the Accountability Gap
Every AI action traces back to the person who reviewed it and why.
Risk Scored on every Action
Stakes, scope, reversibility, and confidence, then routed accordingly.
Oversight with Business Continuity
Only critical actions pause; the rest of the workflow runs.
Continuous Compliance
Generates audit-grade evidence on every action, every day, not just at deployment.
Beyond Policy Gates
Not "is this allowed?" but "who is accountable?" with the context to decide well.
The Human Data Asset
Every review logged with its outcome, making human judgment a measurable, auditable asset.
Enforcement is solved. Authority isn't.
AI agents have crossed the line from suggestion to execution. They write
to production databases, send funds, deny credit, reject candidates and trigger downstream
systems, often within seconds.
The tools to control what they can do have matured.
The infrastructure to govern who is responsible when they act has not.
Agentic AI is in production
Autonomy is no longer a research demo. Agents are taking consequential actions in finance, HR, healthcare, and operations and the failure modes are no longer hypothetical.
Regulators want proof
The EU AI Act and other frameworks increasingly require evidence that human oversight is happening. Static documentation no longer satisfies high-risk.
The accountability gap
When something goes wrong, you need to show who reviewed what, when, and on what basis. Without runtime authority, that evidence doesn't exist.
Alethesis AI slots into the infrastructure you already have.
One integration surface, three deployment options.
Integration
One SDK. One set of keys. One configuration.
- Model-agnostic: AWS Bedrock, Azure OpenAI, GCP Vertex, on-prem, open source
- Framework-agnostic: LangGraph, AutoGen, Google ADK, OpenAI SDK, Anthropic SDK, Vercel AI SDK, Claude Code, or custom
Deployment
No vendor lock-in. Your data, your environment, your choice.
- Managed SaaS: hands-off, up in minutes
- Customer-managed cloud: your own VPC on AWS, Azure, or GCP
- On-premise / bare metal: full data sovereignty, air-gap read
Full Lifecycle
Present from local development to production.
- Development & Testing: Build and iterate safely with mock approvals and simulated workflows.
- Production: Enterprise-grade routing, uncompromised auditing, and real-time oversight.
Architecture Flexibility
Adapts to how your AI operates.
- Workflow Agents: Seamless integration for deterministic, multi-step orchestrated pipelines.
- Autonomous Agentic AI: Dynamic, context-aware oversight for non-deterministic agent actions.
Frequently asked questions.
About Alethesis AI
Is this an observability tool?
No. Observability tools log what happened. Alethesis AI governs what happens, intercepting before execution, scoring and routing to a named human authority. Logging is a byproduct of the architecture, not the product.
Is this a guardrails or eval framework?
No. Guardrails and evals work at the prompt or output level, typically pre-deployment. Alethesis AI works on agent actions, the tool calls and external effects an agent proposes and decides whether each one needs human authority before it lands.
Will it slow our agents down?
The opposite. What slows agents down today is the absence of trustworthy oversight when every action gets the same friction, teams either pull the agent or skip the controls. Alethesis lets routine actions run and brings in a human only when it matters, so you can deploy responsibly without trading speed for quality.
Does it work with our existing oversight workflows?
Yes. Alethesis AI fits around the reviewer roles you already have (risk, compliance, HR, operations) and respects how your organisation already assigns responsibility.
About Runtime Authority
What is runtime authority for AI agents?
Runtime authority is the property that tells an organisation who is responsible when an AI agent acts. It's distinct from runtime enforcement, which tells the system what the agent is allowed to do. Most AI governance tools handle enforcement (rules, guardrails, permissions) but leave authority unaddressed: when an agent takes a consequential action, no qualified person is consulted and no owner is recorded. Runtime authority closes that gap by routing each agent action, before execution, to the human accountable for its outcome.
What's the difference between AI guardrails and runtime authority?
Guardrails define the rules an AI agent must follow - what tools it can call, what data it can access, what outputs are blocked. They answer "is this action allowed?" Runtime authority answers a different question: "who is accountable for this action, and should a human decide before it executes?" Guardrails are necessary but not sufficient: an action can be technically permitted and still warrant human judgment because of its stakes, scope, or reversibility. Runtime authority sits on top of guardrails to ensure consequential decisions reach the right person at the right moment.
What's the difference between human-in-the-loop, human-on-the-loop, and
human-out-of-the-loop?
These three terms describe how much human involvement an AI system has at decision
time.
- Human-in-the-loop (HITL): a person reviews and approves each
action before it executes. Maximum control, minimum scale.
- Human-on-the-loop (HOTL): the AI acts autonomously, but a person
monitors its behaviour and can intervene. Higher scale, but oversight depends on the
human noticing a problem in time.
- Human-out-of-the-loop (HOOTL): the AI acts autonomously with no
real-time human involvement. Maximum scale, no live oversight.
Is human-in-the-loop enough to oversee AI agents?
Not on its own. Traditional human-in-the-loop requires a person to approve every consequential action, which doesn't scale once agents operate at machine speed across multi-step workflows. Effective oversight for agentic AI needs three things classical HITL doesn't deliver: risk-proportional routing, assignment to the right human authority based on context, and an immutable record linking each human decision to the action it governed. The shift is from "human approves everything" to "the right human approves what matters."
Does the EU AI Act require human oversight at runtime?
Yes and this is where many high-risk AI deployments fall short. Article 14 of the EU AI Act requires that human oversight be effective during the period of use, not merely designed at deployment. For agentic systems that act autonomously across multi-step workflows, this means providers must be able to demonstrate that a qualified human reviewed consequential decisions in real time, with traceable accountability. Static documentation and pre-deployment evaluations don't satisfy this requirement. Runtime authority, intercepting agent actions before execution and routing them to accountable humans, is how operational oversight becomes provable.
Who's responsible for your AI agents' actions?
See how Alethesis AI makes every agent decision transparent, risk-assessed, and accountable.
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