You automated your linting. Your test runs. Your deploys. But you still spend half the day on repetitive work.
The problem: you automated tasks, not workflows.
The difference
A task is a single action: run tests, format code, deploy to staging, send a Slack notification.
A workflow is the connected sequence of tasks, decisions, and context switches that accomplishes a goal: receive bug report → reproduce → fix → test → get reviewed → deploy → verify in production → update ticket → notify reporter.
Task automation tools handle the individual steps. Workflow automation handles the sequence, the context, and the decision-making that connects them.
Why developers get this backwards
Tasks are neat, solvable problems. "Automate running tests on push" has a clear input, output, and implementation. Developers are naturally drawn to that.
But your time isn't spent on the tasks themselves. It's spent on the space between them: deciding what to do next, switching tools, remembering where you left off, re-establishing context after an interruption.
Research consistently puts it at roughly 20% task execution, 80% everything else — reading code, understanding context, making decisions, communicating, switching tools.
Task automation optimizes the 20%. Workflow automation targets the 80%.
What workflow automation looks like
Take a bug fix. Without automation, it's ~12 steps across 6+ tools with 10+ context switches: Jira → terminal → IDE → terminal → browser → GitHub → IDE → terminal → monitoring → browser → Slack.
With workflow automation, the AI knows your bug-fix sequence. When you start from a ticket, it pre-loads the relevant code, tracks your progress through steps, and handles the rote transitions. You focus on the fix itself.
Why AI changes the equation
Traditional workflow automation (Zapier, n8n, Temporal) requires manually defining every step and branch. Works for standardized business processes, breaks down for developer workflows — too variable, too context-dependent.
AI-powered workflow automation observes your actual executions and learns the pattern, including the variations. It handles the common case automatically and prompts you only at genuine decision points.
Getting started
- Map your top 5 workflows. Multi-step processes you repeat weekly.
- Identify the context switches. Where do you lose time moving between tools?
- Find the decision points. Which steps need judgment vs. rote execution?
- Record a few executions. The AI needs examples, not specifications.
- Automate the rote parts first. Keep human-in-the-loop for the decisions.
What not to do
Automate everything at once. Start with one workflow. Get it right.
Confuse documentation with automation. Writing down your workflow is step one. But a document doesn't execute itself.
Eliminate human judgment entirely. Good workflow automation keeps you in control at decision points. The goal is to remove the rote parts, not the thinking parts.
What we're building
Distill captures complete work sessions, not individual tasks. It extracts the full sequence — steps, decisions, tool switches — and turns them into reusable skills. The distinction between task automation and workflow automation is baked into the product.
The shift is small but meaningful: stop asking "which tasks can I automate?" and start asking "which workflows can I teach?"