Stop only rewriting prompts when an agent fails: describe the job in plain language, let a builder agent define tools and evals, then retrain the model on real failures and preferred answers: AI agent prompt

SiliconANGLE (Nov 4, 2026) reports that Oumi extended its Frontier AI Engineer (FAiE) so teams can describe in plain language what an agent should accomplish, and FAiE helps define the tools and environments, then moves into evaluation and training. Production failures become evaluation sets and training data for the next round. CEO Manos Koukoumidis argued that prompts and bigger generic models only go so far, and that successful agents improve by training the weights of the models that power them using failed tasks and user preferences. Bring Your Own Harness lets customers keep their existing orchestration, tools and memory and connect to Oumi for evaluation and model training. Oumi added direct preference optimization alongside supervised fine-tuning, and group relative policy optimization (task-outcome reinforcement learning) in private preview, with methods chainable in multistage runs. The company positions specialized owned models against general-purpose frontier APIs for cost and fit. The prompt below gives a practical failure-to-retrain loop you can run even before full weight training is available.

CodingBuilder agent that trains custom agents from failuresadvanced

The prompt

Act as the builder for one failing AI agent. Agent goal: [one sentence]. Current stack: [model, tools, harness]. Top failure modes from production or tests: [paste 5–20 failing transcripts or notes]. Preferred good answers (if any): [paste]. 1) Restate the job, required tools, and hard limits (what it must never do). 2) Turn each failure into an evaluation case with input, expected behavior, and a pass/fail check a script could run. 3) Separate fixes: prompt/harness changes I can ship today vs examples that should become SFT or preference-training data. 4) Write a preference dataset sketch: for 10 cases, a chosen reply and a rejected reply, with why. 5) Propose a retraining or reranking plan (SFT, DPO, or outcome-based RL if I have it) and a soak test before any production cutover. 6) Give a Bring-Your-Own-Harness checklist so my orchestration and memory stay in place. Do not invent tool APIs I didn't describe; mark gaps [MISSING]. Nothing deploys without my approval.
Open in ChatGPT Open in Claude Perplexity Download as agent skill (SKILL.md)

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This prompt has 4 [bracket]s to fill in. Type your details below and the prompt updates as you go. Your answers stay in this browser and are reused on other prompts with the same blanks. Nothing is sent to us.

How to use it

  1. Copy the prompt above.
  2. Replace every [bracket] with your own details (use the fill-in-the-blanks box above, which remembers your answers for the next prompt).
  3. Paste it into Oumi FAiE, Claude Code, Cursor and review the answer before you use it.

Works with: Oumi FAiE, Claude Code, Cursor, your agent harness, eval scripts

Real-world example: siliconangle.com/2026/11/04/oumis-frontier-ai-engineer-now-builds-ai-a

Featured in: AI prompts for writing SOPs, reports, policies and checklists

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