The execution layer that makes AI work in SaaS
Design, deploy, and run AI-powered Jobs of Work reliably across systems, processes, and human interactions.
What makes us different
Most AI solutions stop at generating outputs or automating simple workflows. Aguru is built to run complex, long-running operational work reliably, at scale, inside your SaaS system.
Execution infrastructure, not another AI feature
Aguru is the execution layer your system needs to embed AI in real operations. It enables complex, long-running, exception-heavy processes to run reliably within your system, using AI where it adds real value, without requiring you to build or maintain the infrastructure yourself.
Jobs of Work, not linear workflows
Standard workflows assume clean, predictable paths. Real operations don’t. Aguru executes Jobs of Work, complete, outcome-driven processes that handle exceptions, span multiple systems, and involve human decisions from start to finish.
Built for production resiliency
We assume failure happens and design for it. Retries, checkpointing, long-running state, and human-in-the-loop controls ensure your AI works reliably every time, not just once.
Cross-channel orchestration
Real work spans across various systems and teams. Aguru operates across APIs, browser interfaces, and communication channels like email, coordinating systems and people in a single execution layer.
Enterprise-grade visibility and control
Aguru eliminates the “black box.” Every action produces a full audit trail, providing the transparency and control enterprise customers demand.
How Aguru compares
| RPA | AI agents | Aguru | |
|---|---|---|---|
| What it’s built for | Automating predefined, rule-based tasks | Generating outputs or actions | Executing Jobs of Work |
| Type of operations | Short-lived, single-system tasks | Short tasks or interactions | Long-running, cross-system processes dependent on human decisions |
| Real-world complexity | Rigid, struggles with changes and exceptions | Inconsistent across edge cases | Handles exceptions, human coordination, and failure by design |
| Reliability in production | Breaks when conditions change | Unreliable at scale | Reliable at scale |
| Control & visibility | Rule-based control, limited adaptability | Low visibility and control | Full observability and governance |