Workflow comparisons
The useful question is not whether one category wins every row. Compare context switching, data provenance, review control, operational cost, and failure handling against the work your team actually performs.
Compare team briefsCompare by job to be done
Each brief below follows the same four-layer pattern—brief, agent actions, review gate, and output—while changing the decision criteria for the person accountable for the work.
Define account fit, role coverage, trigger evidence, exclusions, and a manageable review volume.
Research companies, organize available contact context, and flag conflicting or missing evidence.
A manager checks relevance, freshness, and the reason each record should move forward.
An approved research set; messaging and send decisions remain separate.
State the market hypothesis, painful workflow, target company shape, and disqualifying conditions.
Assemble a small sample with fit rationale and questions that would change the hypothesis.
The founder classifies strong, weak, and surprising examples instead of measuring list size alone.
A revised ICP and the next research test, without pretending the sample proves demand.
Specify required CRM fields, accepted sources, freshness thresholds, and ownership for exceptions.
Compare candidate values, preserve provenance, and identify records that cannot be resolved safely.
An operator approves field-level changes and confirms the downstream write scope.
A controlled update file with unresolved items kept out of production automation.
Operating model trade-offs
Useful when teams want prospecting inside an existing AI-agent context and can govern tools, credentials, and outputs.
Useful when teams prioritize a single admin layer, standardized objects, reporting, and managed user access.
Useful for reproducing manual steps where supported APIs do not cover the desired workflow.
Useful when stable contracts, documented rate limits, and auditable request behavior matter most.
| Measured dimension | Agent-native skill | Seat SaaS | Browser automation | API workflow |
|---|---|---|---|---|
| Setup cost | Runtime, package, and provider configuration time | Workspace, seat, role, and data-package setup time | Browser profile, selector, session, and recovery setup time | Authentication, schema mapping, and integration build time |
| Data evidence | Field source, checked-at time, and unresolved state | Exported provenance and vendor refresh disclosure | Page URL, capture time, and extraction rule | Endpoint, response field, request ID, and provider timestamp |
| Control point | Human-in-the-loop review before export | Role permission, approval state, or campaign gate | Pause and inspection before the browser action commits | Write scope, dry run, and idempotency control |
| Failure measure | Unresolved fields, tool errors, and rejected candidates | Import errors, stale fields, and workflow exceptions | Selector failures, session expiry, and incomplete pages | HTTP errors, API rate limits, timeouts, and schema drift |
| Operating cost | Runtime usage plus connected-provider charges | Seats, credits, add-ons, and contract minimums | Maintenance hours, browser infrastructure, and retry load | Engineering hours, API usage, and monitoring load |
A comparison you can reproduce
Public pages rarely reveal the configuration that determines real results. Build a small test brief, document the source set, record rate limits and missing fields, and ask the same reviewer to classify output quality across approaches.
A defensible evaluation tracks not only coverage but also provenance, time spent resolving errors, reviewer effort, export controls, and the effect on sender reputation. Unknown or undisclosed items should remain visible rather than receiving an assumed score.
This is a proposed test protocol, not an OKKI Go performance claim. Use the identical fixture and reviewer across every approach.
Run a bounded evaluation
Copying the text does not run or configure the skill.