Agentic operations, controlled by the people who run them.
Automation gives recurring work structure. AI gives it judgment. Malleable brings the two together around a workflow your domain experts can define, inspect, and improve.
Automation tools
The process gets buried in the wiring of six apps. When something breaks, it's archaeology.
See full comparison →AI agent platforms
The agent can do the work. The harder question is how the team defines, owns, and improves the operation around it.
See full comparison →Custom & vibe-coded builds
The process freezes into code nobody fully understands. Including, soon, whoever wrote it.
See full comparison →Start from the tools your team already knows.
Automation platforms
Zapier, Workato, Parabola, Power Automate, and n8n
Explore categoryAI agent platforms
Claude, ChatGPT, Manus, and OpenClaw
Explore categoryEnterprise agent platforms
Microsoft Copilot Studio, Gemini Enterprise, OpenAI Workspace Agents, Agentforce, and ServiceNow
Explore categoryForm platforms
Fillout
Explore categoryAI teammates
Lindy and Coworker
Explore categoryCustom-building approaches
Internal technical teams, external consultants, and vibe coding
Explore categoryWhat changed is that AI agents can finally do both halves: run real operational work, and build the understanding of the process in the open, where your team can see it and change it.
Raw capability isn't enough. A raw agent drifts; you can't observe it, and you can't iterate on it. What makes it dependable is the scaffold around the agent: the stated process, the guardrails, and the record of every run.
We spent a decade building work management and AI products. Building that scaffold is what we do.
Every stage carries its own guardrails: the outcome, the required actions, the allowed apps, and where the agent can use judgment.