ARI accepts natural-language requests, executes them, and returns results through human approval.
Designed by Clevi so AI pilots become real business processes.
Chatbots and AI execution have proliferated, but they don't connect to actual work workflows — leaving silos. Each tool is managed separately, and without proper governance the work stalls at the pilot stage.
Which documents the AI referenced, what judgment it made, who approved it — no record. In regulated environments or under internal audit requirements, AI tools become hard to use.
When AI has access to high-risk operations like API calls, external system edits, or code deployment, control becomes difficult. Structures that execute without human review are hard for enterprises to accept.
Internal documents, policies, terminology, and prior work records aren't conveyed to the AI — so context has to be explained every single time. Organizational knowledge never accumulates as an AI asset.
You can't see the intermediate state of long-running AI work, and when errors occur it's hard to figure out where it failed. Not a structure you can trust in production environments.
Risky tasks don't execute without human review. Approval policies are set by task type and permission level, and unapproved tasks are blocked automatically.
You can audit what the AI did anytime. From request to result, the full execution history is recorded in a traceable form — fit for internal audit and compliance.
Context is managed as an organizational asset. Internal documents, work records, and judgment rationale accumulate so the AI works from your organization's unique knowledge base.
ARI relays code-execution requests through the server. Clients never need direct access to the execution environment.
When ARI works as a code agent, the files and system scope it can access are restricted by policy.
High-risk commands ARI tries to run only execute after human review. The organization directly controls the scope of automation.
Code, documents, and artifacts generated by ARI are stored together with their execution rationale and history. The provenance of every output is always checkable.
ARI works on files in remote environments, with results viewable and editable in an intuitive UI.
Policies govern the scope and manner in which ARI learns or records memory mid-task — preventing unintended context contamination.
ARI isn't locked to a specific LLM or coding platform. Swap the underlying model or execution environment and the workflow keeps running.
ARI's task state, errors, and retry history are monitored at operations level. No black box — operate AI agents transparently.
ARI isn't yet another agent you add. It's an operations-, permission-, and audit-focused control plane that lets multiple agents and tools execute safely on top of enterprise work.
| Approach | Strengths | Limits | ARI difference |
|---|---|---|---|
| Generic chatbot / RAG | Fast Q&A and document answers | Weak on execution, approval, external action, and result return | Coupled with search |
| IDE coding assistants (Copilot, Cursor) | Personal developer productivity | Easily disconnected from enterprise workflow, remote endpoints, and audit | Code work absorbs Clevi's permission, approval, evidence, and document-return flow naturally |
| Autonomous agents (OpenHands, Devin) | File editing, shell, tests, long-running code work | The agent tends to own memory/tool/session state itself | ARI uses only the execution plane; the server owns policy, memory, approval, and artifact |
| Agent frameworks (LangGraph, AutoGen) | Developers can compose agent flows in code easily | Operations UI, ACL, domain data, and endpoint queue must be built yourself | Complementary capabilities provided as built-in Clevi operations products |
| RPA / classic workflow | Structured-procedure automation and system integration | Weak on unstructured knowledge search, natural-language judgment, and code work | Combines explicit workflow with LLM, AgentLoop, Toolset, Memory, and Code Agent |
* Comparisons reflect general product characteristics; detailed capabilities vary by version and configuration.
