# Keep your agent distribution working after launch

> A practical operating model for ownership, freshness, regression checks, and deciding which channels deserve investment.

Canonical: https://byagents.dev/humans/maintaining-agent-distribution
Published: 2026-09-13
Reviewed: 2026-09-13
Publisher: ByAgents
Type: Playbook
Level: Mixed
Outcome: Set review triggers and a small operating dashboard that connects maintenance to business value.

Agent readiness can decay without a redesign. A price changes, a documentation link breaks, a model stops using a tool, or a platform changes its admission process. Treat the published facts and action path as maintained product surfaces, not a one-time optimization project.

## Give each surface an owner

| Surface | Accountable owner | Trigger for review |
| --- | --- | --- |
| Product facts and policies | Commercial or product owner | Price, scope, region, or terms change |
| Public pages and structured records | Web or content owner | Content release or source-data change |
| Feeds and catalog records | Commerce or integration owner | Inventory updates, rejections, deprecations |
| API and tool interfaces | Engineering owner | Release, schema change, authentication change |
| Discovery experiments | Growth or research owner | Platform change or a new customer task |
| Outcome reporting | Operations owner | Event-definition or attribution-policy change |

A solo founder may hold every role. The value of the table is assigning a decision and a review trigger, not creating departments.

## Make freshness depend on the fact

A founding date rarely needs refreshing. A product description changes with releases. Stock and appointment availability may need much faster updates. Set tolerances based on the cost of a wrong answer and the capabilities of the actual receiving platform.

Separate last modified, last reviewed, and last successfully fetched. None is a substitute for the others. A page fetched today can contain a stale price. A newly edited article can cite an obsolete specification. A review date should record a real review, not update automatically every midnight.

## Check a small set of invariants

Confirm that important public routes still return useful content, recurring facts agree, the intended variant or service survives the handoff, and supported workflows produce a valid final state. Check a failure case too. A change that improves the happy path but creates duplicate paid actions is not a successful release.

Use automated checks where they are meaningful: broken internal links, invalid JSON, missing required feed fields, or a known test fixture returning the wrong artifact. Keep judgment-based checks separate: whether a source is credible, a comparison fair, or a task representative cannot be established by syntax validation alone.

## Keep a change log that supports diagnosis

Record the changed surface, reason, owner, release date, affected consumer, and verification result. If a protocol changes, pin the revision actually tested. If a platform announcement and a current specification disagree, record the conflict and the basis for the implementation choice.

When a result regresses, inspect the earliest failing stage. Was the page no longer retrieved, was the product misunderstood, or did the action fail? A single “visibility score” cannot tell an engineer which of those to repair.

## Review investment using actual outcomes

A useful monthly discussion has five inputs: work completed, maintenance cost, observed exposure, verified task completion, and qualified customer activity. Show unknown attribution explicitly. Include support burden and failed or refunded transactions when evaluating the economics of an integration.

Decide whether to maintain, expand, revise, or retire each experimental route. A channel with little new acquisition may still help existing customers use the product; record that as adoption or retention value. A channel with many bot requests and no evidence of useful activity may not deserve more content production.

## How this library handles freshness

ByAgents publishes a review date on each document, links primary sources, and separates examples from measured results. Our [current briefing](https://byagents.dev/humans/agent-distribution-now) is a dated editorial snapshot. We do not claim continuous monitoring or comprehensive testing of every assistant.

Our own HTML, Markdown, and search use the same content records, and release checks inspect those representations together. That is a concrete publishing practice, not evidence that an assistant will rank us first. Use the [source policy](https://byagents.dev/agents/evidence-policy) and [source review recipe](https://byagents.dev/agents/source-review-recipe) when adapting the approach to your own library.

## Related reading

- [Agent distribution: what matters now](https://byagents.dev/humans/agent-distribution-now)
- [Recipe: review a technical claim and resolve conflicting sources](https://byagents.dev/agents/source-review-recipe)
- [Pattern: measure agent activity without inventing attribution](https://byagents.dev/agents/attribution-event-contract)