Question 1

What is agent fine-tuning?

Accepted Answer

Agent fine-tuning is the process of specializing a base language model for the specific workflows an AI agent must perform - using your private data, tools, and success criteria - so the agent is more accurate, cheaper, and more reliable than a general-purpose frontier API. PerceptEye automates this with three specialist agents and a simulation engine, so your AI, ML, and data teams own the resulting private model.

Question 2

What is hill climbing in AI agent training?

Accepted Answer

Hill climbing is an iterative optimization strategy: the agent is evaluated against a measurable objective, small improvements are kept, and the model repeatedly moves toward higher performance. PerceptEye runs hill-climbing loops against a simulation that provides consistent ground truth, so every training iteration is scored on the work you actually care about and the model provably gets better over time.

Question 3

What is a continual learning platform for AI agents?

Accepted Answer

A continual learning platform lets an AI agent keep improving after deployment instead of staying frozen. PerceptEye closes the loop between production signals, simulation, reward modeling, and re-training so private agents adapt to new data and workflows continuously - making continual learning native to every agent.

Question 4

Why train a private AI model instead of calling a frontier API?

Accepted Answer

Calling frontier APIs directly for repetitive, knowledge-intensive workflows is expensive, slow, leaks data, and produces unpredictable quality. A private model trained with PerceptEye runs where you choose, keeps your data in your environment, and can match frontier-grade accuracy on your tasks at up to 100x lower inference cost.

Question 5

What is the difference between supervised fine-tuning (SFT) and reinforcement learning (RL)?

Accepted Answer

SFT teaches a model to imitate labeled examples and is the fastest way to lock in format, tone, and known-correct behavior. RL optimizes against a reward signal instead of fixed labels, so the model can discover better strategies for goals that are easy to score but hard to demonstrate, such as passing a test suite or resolving a ticket. Modern post-training runs SFT first to establish a baseline, then applies RL (RLHF, RLAIF, or verifiable-reward methods like GRPO) to push quality beyond what demonstrations alone can reach.

Question 6

How do fine-tuned models compare to frontier models on enterprise benchmarks?

Accepted Answer

Public leaderboards like MMLU, GPQA, and SWE-bench measure broad general capability, so large frontier models usually top them. On a narrow enterprise workflow, a well-tuned smaller model routinely matches or beats a frontier API on task-specific accuracy while running far cheaper and faster. The benchmark that matters is a held-out evaluation built from your own data and success criteria, which is why PerceptEye optimizes against a task-specific eval instead of generic leaderboard scores.

Question 7

How much data do we need to fine-tune a model for our use case?

Accepted Answer

Less than most teams expect. A few hundred to a few thousand high-quality, representative examples is often enough for supervised fine-tuning, because coverage of edge cases matters more than raw volume. When labeled data is scarce, simulation and synthetic generation expand coverage and RL against a reward function reduces the need for hand-labeled outputs entirely. Start with the data you already have, measure against a real evaluation, and add targeted examples only where the model is weak.

Question 8

How do you prevent model drift and catastrophic forgetting after deployment?

Accepted Answer

A model frozen at ship date decays as data, tools, and workflows change. PerceptEye treats improvement as a continuous loop: production signals feed back into simulation and re-training, every candidate is scored against a stable evaluation before promotion, and prior capabilities are protected by mixing earlier tasks back into training and gating releases on regression checks, so the model adapts without silently losing skills it already had.

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