Failproof AI

Failproof AI turns agent failure detection into a billable governance layer, letting agencies charge for reliability rather than just build time. With a $0 entry tier and $99 Team plan, agencies can monitor client agents cheaply, then scale audits and policy enforcement as client agent counts grow, compounding retainer value.

Failproof AI's pricing scales by monthly runs, from the Free Forever tier at $0 for 5,000 runs to the Team plan at $99 for 50,000 runs. Agencies deploying agents for clients can map each client's expected run volume to the appropriate tier, ensuring they never exceed the hard cap on the free plan, which would halt monitoring. For example, a clinic's appointment-booking agent might generate 3,000 runs monthly, fitting the free tier, while a law office's document-review agent could hit 20,000 runs, requiring the Team plan. The ladder framework helps agencies decide whether to absorb the $99 cost as a delivery expense or pass it through as a line item in the client retainer. By aligning client run volume with the correct tier, agencies avoid surprise overage gaps and maintain continuous failure auditing, which is critical for client trust.

Eval debt is the accumulated gap between what an AI agent does in production and what anyone on the agency team can actually prove it does. Like technical debt, it accrues quietly and charges interest: every untraced failure mode, every scoring rubric that lives in a Slack thread, every client demo that worked once and was never re-run. The interest payment arrives as a retainer conversation. Agencies that instrument early convert that debt into a premium line item, because "production-ready" is a claim only evidence can support. The cost curve is moving in their favor: OpenAI cut GPT-6 Sol and Luna API prices 50% versus GPT-5.6, and prompt caching now discounts up to 90% on reused prefixes, so high-volume agent pipelines are cheaper to run and cheaper to trace. Meanwhile Forrester's 2027 predictions flag compute and infrastructure constraints that will push API-dependent tool costs upward, compressing margins on AI-inclusive retainers. Tracing spend is the hedge.

The Silent Failure Surface is the set of AI behaviors that pass every automated check yet still damage the client relationship: a voice agent that interrupts callers, a support bot that loops a user through three retries, a research agent that returns confident but stale answers. Standard evals score outputs against expected answers, so they miss friction that only appears in live sessions. Agencies that map this surface before launch can price a monitoring retainer against it; agencies that skip it discover failures when the client forwards a complaint. Cekura simulates thousands of personas to expose interruption and gibberish patterns before go-live, while Agnost AI ingests real conversations and flags repeated retries and broken workflows as actionable intents. Both approaches treat production traffic as the primary test set, not a post-launch afterthought. The surface shrinks only when someone owns the loop between detection and a shipped fix.

Adopt Failproof AI only when you have 3 or more active client agent projects and your monthly run volume exceeds 5,000, then start on the Free Forever tier to validate before committing to the $99 Team plan.

Instrument the AI workflow with tracing and scoring before you defend its output quality to a client.

If your agency runs 3+ active client agent projects and needs runtime failure detection across Claude Code, Cursor, or Codex, start with the Free Forever tier ($0/mo, 5,000 runs) to validate tracing and policy enforcement. Upgrade to Team at $99/mo for 50,000 runs and unlimited failure audits only when client volume demands it; otherwise, defer if you lack integration capacity or need white-label client portals.

A delivery sprint that configures Failproof AI runtime monitoring, policy enforcement, and failure auditing for a client's AI agents, giving the agency a repeatable observability offer.