Engram uses AI to build browser workflows once, then replays them deterministically. No LLM calls per run. No token costs. ~3s execution instead of 30s+.
AI agents make an LLM call for every browser action. That's slow, expensive, and non-deterministic. It doesn't scale.
Traditional automation (RPA) runs fast and scales well, but workflows are tedious to build and brittle when UIs change.
Engram uses AI once to create a workflow, then compiles it to run without AI. When something breaks, the agent takes over to repair and recompile. Easy to build. Cheap to run. Deterministic at scale.
Describe your task. An AI agent navigates the browser, clicks, types, handles auth. You watch it work once.
Engram converts the successful run into a versioned workflow with stable selectors and parameterized inputs.
The workflow runs directly via Playwright. No LLM in the loop. Same result every time.
When a site's UI changes and a step fails, Engram hands control back to the agent to fix it, then compiles the updated workflow.
| RPA | AI Agents | Engram | |
|---|---|---|---|
| Setup | Manual | Natural language | Natural language |
| Execution | Deterministic | Non-deterministic | Deterministic |
| Speed | ~3s | 30s+ | ~3s |
| Cost per run | ~$0.01 | $0.20+ | ~$0.02 |
| UI changes | Manual fix | Retry heuristics | Auto-repair |
Workflows reference secret IDs, not values. Credentials are encrypted at rest, injected at runtime, and scoped per-workflow. Envelope encryption with short-lived tokens.
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