Workspace
Keep chat, launchers, recent runs, and resumable sessions in one place
Workspace gives operators and teams a first-class surface for active conversations, workflow launches, recent work, and continuation state.
Product
The platform for governed AI execution.
Atellagent gives teams the platform for governing workflows, tools, models, channels, and AI-driven actions before execution, with evidence-backed decisions and one control model across hosted and external runtimes.
This is where the governed execution model becomes an operational system teams can deploy, review, and expand over time.
Core Product
The product surfaces teams use to run, review, and govern AI execution.
Dashboard
See governance queues, review backlog, change windows, and workflow activity at a glance
Dashboard summarizes decision volume, deny rate, open review work, queue presets, recent governance changes, and workflow sessions that need attention.
Workflows
Design workflows, deploy them, inspect executions, and manage sessions
Workflow management keeps design, deployments, executions, and session state together so automation rollout and runtime operations stay in one surface.
Policies
Manage policy data, rules, roles, and canonical decision review
Policy Management separates data, rules, and roles, then adds a Decisions & Review workspace with queues, filters, exports, and adjudication.
Visibility
Review alerts, telemetry, security audit events, monitoring, and side effects
Visibility keeps event history, audit activity, monitoring, and side-effect records in one place for investigation and operational review.
Organization
Manage identities, ownership, and connected systems
Manage users, roles, enterprise SSO, service accounts, provider access, connected channels, and runtime inventory from one operating surface.
Deployment And Integration
Use Atellagent as the hosted runtime, connect external runtimes, or run both together.
Choose the deployment mode that fits your environment without changing how action requests are evaluated, authorized, and recorded.
Hosted Runtime
Run first-class agents and automations directly in Atellagent
Use Atellagent as the managed runtime when you want the strongest built-in execution-governance posture.
Adapter Participation
Keep an external runtime while Atellagent governs the execution contract
Connect customer-owned execution without rebuilding policy, evidence, and review around a separate stack.
SDK Participation
Embed governed outbound calls directly into an existing process
Keep runtime ownership where it is while standardizing how governed calls leave the process.
Models And Detectors
Bring models and detectors under the same authorization model.
Atellagent treats models and detector-driven controls as governed participants in the execution path, not bolt-ons around it.
Custom Models
Use built-in provider integrations or add custom model endpoints
Bring your own models into the product without rewriting the governed path around each provider.
Model Access
Control model access with policy rather than one-off application logic
Keep model governance aligned with identity, action type, environment, and runtime context.
Detector-Driven Controls
Use detectors for prompt risk, response release, and organization-specific protections
Atellagent includes built-in detectors for protections like PII and prompt injection, and lets teams add classifiers for risks such as IP, trade secrets, or internal data policies.
Rollout Path
How teams move from visibility to enforcement.
Teams often start with governed visibility, validate decisions against live workloads, and then tighten into enforcement as confidence grows.
Land
Bring identity, decision evidence, and policy evaluation online
Start with governed visibility instead of day-one hard blocking.
Observe And Tune
Run decisions in advisory mode and refine policy against real workloads
Tighten by action class and risk surface as evidence accumulates.
Enforce And Expand
Turn on blocking gradually, then extend the model across more action classes
Begin with one critical workload, then expand the same control model across automations and other enterprise AI systems.
High-Impact Starting Workloads
Where teams often prove the model first.
Many teams first prove the model on coding agents or other high-impact workloads where the actions are immediate, visible, and easy to evaluate under governance.
Coding Agents
Govern agents that read code, modify files, and use high-impact tools
Start where automated work already touches repositories, patches, and development systems, then bring those actions under one governed decision path.
Governed Tool Access
Keep tool calls inside approved routes and bounded surfaces
Apply one control model to tool use so teams can standardize what actions are allowed, what systems are reachable, and how those outcomes are reviewed.
Broader Expansion
Extend the same model across workflows, communication channels, and enterprise automations
The initial workload does not define the platform. It proves the control model before teams expand it across other high-value AI systems and governed interaction surfaces.
Walk through the product, then go deeper on the runtime boundary.
A product demo covers deployment modes, governed execution surfaces, and the adoption path. The Architecture page explains the deeper control model behind it.