Your AI agent fails silently. Find out why in 48 hours, $49.
Send your agent prompts, code, and one example failure. You get back a 20 to 30 minute Google Meet walkthrough plus a 2-page Markdown report identifying your top three failure modes with concrete fixes.
Refund if no fix lands inside 14 days.
5 spots per week.
- examples/sample-audit.md — anonymised real audit deliverable. The actual format and depth you receive.
- examples/silent-tool-call-contract.md — proof of concept LOGIC.md contract that catches the single most common failure mode I find in production agents. Includes vitest fixtures.
These are the artifacts. Read them before you book.
flowchart LR
A[Buyer emails<br/>info@singlesource.co.za]:::buyer --> B[Pay $49]:::pay
B --> C[Send intake<br/>prompts + 1 failure]:::intake
C --> D[I review<br/>48 hours]:::work
D --> E[Google Meet + Report<br/>+ Contract stub]:::deliver
E --> F{Fix lands<br/>in 14 days?}:::check
F -->|Yes| G[Done]:::win
F -->|No| H[Full refund]:::refund
classDef buyer fill:#1a1a2e,color:#fff,stroke:#1a1a2e
classDef pay fill:#d97757,color:#fff,stroke:#d97757
classDef intake fill:#1a1a2e,color:#fff,stroke:#1a1a2e
classDef work fill:#1a1a2e,color:#fff,stroke:#1a1a2e
classDef deliver fill:#1a1a2e,color:#fff,stroke:#1a1a2e
classDef check fill:#fafaf7,color:#1a1a2e,stroke:#1a1a2e
classDef win fill:#2d6a4f,color:#fff,stroke:#2d6a4f
classDef refund fill:#9d0208,color:#fff,stroke:#9d0208
Rainier Potgieter, maintainer of LOGIC.md: open-source declarative reasoning layer for AI agents (325 tests, 95.9% branch coverage on the compiler, MIT licensed, validated through Modular9 in production).
- You ship LLM agents. They pass your evals and break in production.
- You suspect contract drift but nobody on the team has formalised the reasoning DAG.
- You don't have a senior who has shipped ten of these and seen the failure modes recur.
We walk through your agent prompts, code, and example failures together. I name the failure modes live so you can ask follow-ups in the moment. Recording sent to you afterward.
Top three failure modes ranked. Each comes with a fix recommendation and code or contract examples you can apply this week. See examples/sample-audit.md for the real format.
If your stack benefits, I write a reasoning contract that makes the top failure mode structurally impossible. See examples/silent-tool-call-contract.md for the real format.
flowchart TD
Root([5 most common agent failure modes]):::root
Root --> F1[Contract drift<br/>Behaviour drifts from prompt's stated contract]:::fail
Root --> F2[Silent tool-call failures<br/>Tool returns error, agent treats as success]:::fail
Root --> F3[Retry-loop blow-ups<br/>Cost spikes from poorly-bounded retries]:::fail
Root --> F4[Planning-without-doing<br/>Agent describes the task instead of executing]:::fail
Root --> F5[Eval-prod divergence<br/>Passes test fixtures, breaks on real input]:::fail
F1 --> Fix1[Fix: pre/post assertions on contract boundary]:::fix
F2 --> Fix2[Fix: structured retry policy + outcome assertion]:::fix
F3 --> Fix3[Fix: bounded retry with exponential backoff]:::fix
F4 --> Fix4[Fix: execution mandate + output contract]:::fix
F5 --> Fix5[Fix: trajectory eval, not just final output]:::fix
classDef root fill:#1a1a2e,color:#fff,stroke:#1a1a2e
classDef fail fill:#9d0208,color:#fff,stroke:#9d0208
classDef fix fill:#2d6a4f,color:#fff,stroke:#2d6a4f
| Signal | Source |
|---|---|
| 32% of teams cite quality as the top barrier for agents in production | Datadog, State of AI Engineering 2026 |
| Final-output evals miss 20-40% of agent failures vs. trajectory eval | LangChain, State of Agent Engineering 2026 |
| 8.4M rate-limit errors logged across LLM call spans, March 2026 alone | Datadog State of AI Engineering 2026 |
| Mastra (TS-first agents) hit 150k weekly npm downloads, $13M seed | Public reporting |
If your team is in those numbers, this audit is for you.
Why $49 when Claude Code Pro is $20 per month? Claude Code is brilliant for pair-programming on your own code. This audit gives you something different: external pattern-matching from someone who has shipped LOGIC.md (the OSS reasoning layer for AI agents) and reviewed dozens of agent failures. The same 5 failure modes recur across every team. I name yours in 5 minutes where Claude Code might miss them or take an hour. Two and a half months of Claude Code Pro for one shipped fix you would not have caught yourself.
What if my framework is exotic? I cover vanilla OpenAI/Anthropic API, LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Mastra, Vercel AI SDK, and custom orchestration. If your stack is genuinely too far outside that surface and I cannot help, full refund inside 24 hours of intake.
Refund policy? Full refund if no fix recommendation lands inside 14 days of payment. Email and I refund.
Will you sign an NDA? Yes. Send yours with the intake.
How it runs (the details)
- Email to book. Receive intake checklist within 5 minutes.
- You send: agent prompts, one or two example failures, repo link or relevant code excerpt.
- We schedule a Google Meet inside 48 hours of intake. Recording and report sent after.
Email info@singlesource.co.za with subject "Reasoning Audit booking" and I send payment instructions plus the intake checklist.
5 spots per week. When the week's spots fill, listing closes until next Monday.