Self-Improving Agentv1.0.0

A founder's AI assistant handles email drafting, calendar scheduling, and meeting prep. Each time the founder corrects tone, format, or process, the agent logs it to RULES.md so the next draft or invite already matches their preferences without re-explaining.

A support team deploys an AI agent across helpdesk tickets. When a human edits a response — say, adding a refund policy detail or adjusting empathy — the agent records the pattern and applies it to similar future tickets, steadily reducing edits per reply.

A marketing agency runs an AI writer on client content briefs. Every round of editor feedback becomes a dated rule — brand voice quirks, banned phrases, CTA conventions — so the agent's first drafts get approved faster each week.

A healthcare front-desk agent schedules appointments and answers patient questions. Corrections about insurance quirks, provider availability rules, and preferred phrasing get codified so the agent stops repeating errors across locations.

An engineering team's agent drafts PR descriptions, incident summaries, and runbook updates. Reviewers' corrections about formatting, required links, and escalation language are logged so the agent aligns with team conventions over time.

Sell a domain-tuned self-improving agent (e.g., support, legal intake, agency copy) as a subscription. The correction loop becomes a retention moat: the longer a customer uses it, the better it fits their workflows, making switching costly.

Charge clients to set up the self-improving agent loop inside their existing tools — writing initial RULES.md, wiring AGENTS.md, and running a monthly rules-review cadence. Ongoing retainer covers tuning and rule hygiene.

Offer a hosted service that stores, versions, and syncs RULES.md-style memory across an organization's agents, with dashboards for correction frequency and rule conflicts. Positions the correction loop as shared infrastructure rather than per-agent config.