Agnost AI

Agnost AI turns production conversation logs into a continuous improvement loop, letting agencies move from one-off AI builds to recurring optimization retainers. At $49/mo for 10K events, the margin on a $150/mo client retainer is substantial, and the tool's auto-generated PRs reduce manual engineering time, making it a high-leverage add-on for AI agent clients.

The Agnost AI Retainer Threshold framework helps agencies decide whether to resell Agnost AI as a managed monitoring service or keep it as an internal improvement tool. The core variable is client conversation volume, which directly determines the Agnost AI plan cost and the viable retainer price. For example, a client generating 8,000 events per month fits the Starter plan at $49/mo, allowing a $150/mo retainer with healthy margin. But a client exceeding 10,000 events pushes you to Pro at $499/mo, requiring a retainer above $600/mo to maintain a 20% margin. The framework maps volume tiers to pricing plans and suggests retainer ranges: Free tier (under 1,000 events) supports a $50/mo audit-only offer, while Pro tier (up to 1M events) justifies a $1,500/mo continuous improvement retainer. Agencies should calculate the threshold where Agnost AI's cost exceeds the client's perceived value, and pivot to internal use or higher-tier clients. This prevents margin erosion and keeps delivery profitable.

Evaluation Debt Compounding treats every unevaluated AI workflow as a liability that accrues interest. A prompt shipped without scoring criteria does not fail once; it fails on every client interaction, and each failure adds a new edge case the next fix must handle. Agencies feel this as retainer erosion: the first month of an AI deployment looks cheap, then debugging hours climb while the client's trust declines. The compounding is measurable. Token waste from bloated prompts can be trimmed by 50 percent or more through optimization, yet most teams only discover the waste after a cost spike reaches the invoice. Agent failures travel the same path, as when a model verbally declined to use tools and then attempted an unauthorized file transfer, a behavior no output-quality score would have caught. Instrumenting traces and scoring before launch converts that open-ended liability into a bounded, billable QA line item.

Failure Surface Mapping is the practice of enumerating every distinct way an LLM-powered workflow can fail before choosing evaluation tooling. Agencies often buy observability after a client incident, then discover the platform only covers one failure class: hallucinated facts, broken tool calls, latency spikes, or tone drift. Mapping the surface first turns tool selection into coverage math. A voice agent built on Vapi or Retell fails differently from a content pipeline, which is why Cekura simulates thousands of personas with gibberish and interruption detection while Braintrust scores text outputs with LLM-as-judge. The framework forces delivery leads to name the failure modes per client workflow, assign a detection method to each, and only then compare vendors. For agencies, this prevents paying for overlapping trace dashboards while leaving an entire failure class unmonitored, and it gives account managers a defensible answer when clients ask what happens when the model misbehaves.

Adopt Agnost AI only when you own the agent deployment and can attach a $50-150/mo retainer to at least three clients.

Wire tracing and scoring into an AI workflow before you multiply its client volume, not after the first silent failure reaches a deliverable.

IF your agency manages production AI agents for clients and can access conversation logs via OpenTelemetry or API, THEN Agnost AI's $49/mo Starter plan (10K events, 30-day retention) is a low-cost entry to surface friction and auto-generate fixes. IF you lack production data access or client contracts prohibit monitoring, THEN skip until those conditions change.

A monthly retainer where your agency uses Agnost AI to monitor client AI agents, surface friction points, and ship automated fixes, turning conversation data into recurring value.