vsLindy.aiLindy.ai builds AI agents that automate tasks through natural language and reasoning. But AI agents are non-deterministic — each run can behave differently. Stepwork uses deterministic replay with AI self-healing: the same workflow runs the same way every time, with 98% accuracy. Purpose-built for IT provisioning. Enterprise security. Local execution.
Compare automating user provisioning with deterministic vs AI-agent approaches.
AutomationThe capabilities that make Stepwork fundamentally different.
Stepwork replays a recorded workflow the same way every time — with AI vision only for self-healing when the UI changes. Lindy.ai agents reason from scratch on each run, producing variable outcomes that aren't suitable for production provisioning.
Stepwork is designed for user provisioning, access management, onboarding, and offboarding. Lindy.ai is a general-purpose AI agent platform — not optimized for the precision and auditability that IT workflows require.
Stepwork achieves 98% flow accuracy through deterministic replay. Lindy.ai agents rely on LLM reasoning for each action — accuracy varies by task complexity and model behavior.
Stepwork runs in a Docker container on your device. Lindy.ai processes agent actions through its cloud — credentials, PII, and workflow data pass through external servers.
Stepwork uses AI vision only when the UI has changed — to find elements and adapt. Lindy.ai uses AI for every step, which is powerful for open-ended tasks but unreliable for structured provisioning.
Stepwork authenticates through Okta, Entra ID, Google Workspace, and 1Password with native MFA. Lindy.ai connects to tools via OAuth and API keys — no native IDP integration for enterprise SSO flows.
How Stepwork and Lindy.ai handle your data, credentials, and access.
Stepwork runs inside a hardened Docker container on your device. Data never leaves your machine.
StepworkLindy.ai agents execute through its cloud infrastructure. Task data, tool calls, and context flow through Lindy's servers.
Lindy.ai riskStepwork authenticates through your existing identity provider with native MFA. No OAuth tokens stored in third-party platforms.
StepworkLindy.ai stores OAuth tokens and API credentials for connected tools on its platform.
Lindy.ai risk| Feature | Stepwork | Lindy.ai |
|---|---|---|
| Approach | Deterministic replay + AI self-healing | AI agent reasoning per action |
| Accuracy | 98% flow accuracy | Variable — depends on AI reasoning |
| Provisioning use case | Purpose-built | General-purpose agent |
| MFA / SSO | OTP, passkeys, push — native | OAuth / API keys |
| Data handling | Processed locally | Cloud-processed |
| Audit trail | Deterministic, reproducible | AI reasoning — not reproducible |
| Self-healing | AI vision when UI changes | AI adapts — but non-deterministic |
| Enterprise security | Local, hardened container | Cloud-based |
When a UI changes, most automation tools fail silently or require manual fixes. With Stepwork, flows self-heal. AI detects layout shifts, finds the right elements, and keeps the automation running — no human intervention required.
No. Stepwork uses deterministic replay — it records a workflow and replays it the same way every time. AI is used only for self-healing when the UI changes. Lindy.ai uses AI reasoning for every action, which is flexible but non-deterministic and less suitable for production provisioning.
Provisioning requires precision: the right user, the right app, the right role, every time. AI agents can make different decisions on each run. Stepwork's deterministic approach ensures 98% accuracy and full auditability — critical for IT compliance.
Lindy.ai connects to tools via OAuth and API keys. It doesn't authenticate through your identity provider or complete MFA natively. Stepwork signs in through Okta, Entra ID, or 1Password and handles OTP, passkeys, and push notifications.
Stepwork processes all data locally in a hardened Docker container. Lindy.ai routes agent actions and data through its cloud. For provisioning workflows involving PII and credentials, Stepwork's local-first model keeps sensitive data on your infrastructure.
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