Ship One Small AI Feature Before Building an Entire Agent Platform
A focused AI feature is easier to evaluate than an ambitious system with many tools and unclear success criteria. Choose a recurring user problem with a visible result. The first release should prove that the feature helps someone complete a task, including review and recovery when it fails.
Why it matters
Complexity can hide weak value. If a simple suggestion feature does not save useful effort, adding orchestration, memory and more agents may increase maintenance without fixing the underlying product problem.
A practical workflow
- Interview a likely user about one repetitive task and observe how they currently complete and check it.
- Define a narrow input and output, plus a clear fallback when the model cannot produce a useful result.
- Build a prototype with minimal tools and evaluate it on real examples before connecting additional systems.
- Release to a small audience, collect failures and measure accepted results rather than only request volume.
An example
A community organiser might need help categorising event feedback. Start with suggested labels that the organiser can accept or edit. Automated replies, calendar updates and long-term memory can wait until the basic categorisation earns its place in the workflow.
A mistake to avoid
Avoid measuring adoption only through curiosity clicks. People may try an AI button once without using the result. Ask whether the output changed the task and whether the user would miss the feature if it disappeared.
Try it and check the result
Compare the original workflow with the prototype on a few complete tasks. Include checking, corrections and failure recovery. Expand the feature only when the finished-task benefit is clear.
Reference and further reading
Anthropic: building effective agents.
The workflows and examples in this guide are illustrative. Product controls and availability can change; check the linked official guidance for your setup.
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