Building AI Agents with high specialization, consistency, and efficacy 🤖

Building AI agents?

You've likely categorized problems: base model vs prompting vs fine-tuning.

For example, sycophancy is mostly a base model-level alignment issue (with additional mitigation from prompting). See: GPT 5's drop to <6% rate of sycophantic replies, vs 14.5% prior.

But if you're bridging gaps between general LLM competence & highly specialized agents?

Fine-tuning problem.

Multi-agent & synthetic dataset generation frameworks are all the rage now, for good reason.

Specifically, when paired with training techniques like supervised fine tuning & direct preference optimization, they can yield 2-3x improvements in highly specialized tasks. See ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recovery for source in a therapeutics context.

And this is where the value sits for anyone building sufficiently specialized agents over the next few years.

Planning to share more of this, especially as I apply the latest in building AI agents in emotional intelligence for our products.

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I'd love to hear your experience building agents on X (formerly Twitter) or LinkedIn.