Start by Watching Yourself Work
Before automating anything, spend a week documenting your manual workflows. Every time you do something repetitive, write it down. What triggers it, what steps you take, what tools you use, what decisions you make, and what the output looks like. This inventory is your migration candidate list.
You'll find three categories: tasks that are purely mechanical (great candidates), tasks that require judgment calls (partial candidates), and tasks that are mostly creative or strategic (not candidates). Focus on the first category initially. Once you've built confidence, you can tackle the partial candidates where an agent handles the mechanical parts and escalates the judgment calls to you.
Pick the Lowest-Risk Candidate First
Don't start with your most important workflow. Start with something where mistakes are cheap and easy to fix. A report that gets generated weekly. A data sync that runs daily. A set of notifications that get sent based on conditions. These are low-stakes automations where you can experiment, make mistakes, and iterate without consequences.
The goal of your first automation isn't to save the most time. It's to learn the patterns: how to connect MCP servers, how to prompt the agent effectively, how to handle errors, and how to monitor the results. Once you've learned these patterns on a low-risk workflow, applying them to higher-stakes ones gets much easier.
The Shadow Phase
Before handing off a workflow, run the agent in shadow mode. The agent does the work, but instead of executing the results, it shows you what it would have done. You continue doing the work manually, but you compare your output to the agent's output. Where they match, you build confidence. Where they differ, you investigate why.
This shadow phase catches problems that testing alone doesn't find. Maybe the agent handles 90% of cases perfectly but mishandles a specific edge case that occurs every Tuesday because of how the source data is formatted. You wouldn't find that in a test suite, but you'll catch it during the shadow period.
Gradual Handoff
Once the shadow phase shows consistent results, hand off gradually. Let the agent handle the straightforward cases automatically while you continue handling the exceptions manually. Over time, expand the scope of what the agent handles as you identify and resolve edge cases.
This isn't about trusting the agent more over time. It's about making the agent more capable over time by fixing the gaps that the shadow phase and gradual handoff reveal. Each edge case you address makes the automation more robust.
Monitoring After Migration
Automation isn't "set and forget." Monitoring the agent's work is ongoing. Did the data source change? Did the API the agent calls update its response format? Did a new edge case appear that the agent hasn't seen before? Regular audits of your agent connections and periodic review of agent outputs catch problems before they compound.