Swamp
When an AI agent solves a problem, the reasoning behind that solution typically evaporates once the session ends. Nobody else on a team sees how the fix was reached, and nobody can prove afterward what actually happened during the run. Swamp is an automation platform built to close that gap: it captures the way an agent solved a problem and turns it into a versioned, schema-validated workflow that can be run again with the same reliability, rather than re-derived from scratch each time.
Once captured, a workflow can be chained into multi-step automations and triggered on a schedule or by a webhook. Every execution is recorded in an immutable, searchable run history covering both inputs and outputs, which gives teams an audit trail for domains where accountability matters — incident response, data pipelines, customer support, cybersecurity recovery, and infrastructure management are the use cases the platform names. Credentials needed by a workflow are injected in encrypted form at run time, so they never appear inside an AI prompt. The platform is free for individual use, with paid tiers for team collaboration.
Swamp is placed in Assess. The problem it targets — one-off AI reasoning that a team can't reuse, audit, or trust — is real and growing as more teams put agents into operational workflows, but there is no first-person production use to date, which is the bar for Trial.
