#AI Research

Articles about AI Research — exploring patterns, best practices, and real-world implementations in production systems.

9 posts tagged with ai research. ← All posts

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Pratik Dhanave · ·5 min read

Limits, Risks, and Open Problems

The frontier is genuinely exciting and genuinely oversold, and telling the difference matters — so this closing post is an honest accounting of what self-evolving agents cannot yet do, what can go wrong, and what remains unsolved.

The frontier is genuinely exciting and genuinely oversold. An honest accounting of what self-evolving agents cannot yet do, what can go wrong, and what remains unsolved.

#Self-Evolving Agents#AI Research#AI Security#AI Engineering#AI Agents
Pratik Dhanave · ·5 min read

Evaluating Open-Ended Improvement

Every frontier method is a search, and a search is only as good as the evaluator that ranks its candidates — so at the frontier, evaluation stops being a measurement and becomes the single most dangerous component in the system.

Every frontier method is a search, and a search is only as good as the evaluator that ranks its candidates — so at the frontier, evaluation becomes the single most dangerous component in the system.

#Self-Evolving Agents#Evaluation#AI Research#AI Security#AI Agents#AI Engineering
Pratik Dhanave · ·6 min read

Alignment: RLHF and DPO

Supervised fine-tuning teaches a model to produce a correct answer; alignment teaches it to produce the answer people actually prefer. That shift — from "right" to "better" — is what turned raw language models into helpful assistants, and the move from RLHF's complex machinery to DPO's direct approach made it something ordinary teams can do.

Supervised fine-tuning teaches a model to produce a correct answer; alignment teaches it to produce the answer people actually prefer. That shift turned raw models into helpful assistants, and the move from RLHF to DPO made it something ordinary teams can do.

#Fine-Tuning#LLMs#AI Engineering#AI Research
Pratik Dhanave · ·5 min read

Meta-Agents and Self-Reference

The deepest form of self-evolution is recursive: not an agent that improves its answers, but an agent that improves the process that improves agents — a system reaching up a level to modify itself.

The deepest form of self-evolution is recursive: not an agent that improves its answers, but an agent that improves the process that improves agents — a system reaching up a level to modify itself.

#Self-Evolving Agents#AI Research#AI Agents#AI Engineering
Pratik Dhanave · ·5 min read

Reflective Optimizers: Learning in Language

What if the optimizer's update step were not a numeric gradient but a paragraph of natural-language reflection? That is the bet behind reflective optimizers — and one of them rivals reinforcement learning while using a fraction of the rollouts.

What if the optimizer's update step were not a numeric gradient but a paragraph of natural-language reflection? That is the bet behind reflective optimizers — one of which rivals RL with a fraction of the rollouts.

#Self-Evolving Agents#AI Research#AI Engineering#Prompt Engineering#AI Agents
Pratik Dhanave · ·5 min read

Self-Play and Co-Evolution

The technique that produced superhuman game-playing — a system improving by competing against copies of itself — has an LLM analogue: models that generate their own training signal and bootstrap their way up without new human labels.

The technique that produced superhuman game-playing has an LLM analogue: models that generate their own training signal and bootstrap up without new human labels — and the reward-source problem at its center.

#Self-Evolving Agents#AI Research#AI Engineering#LLMs#AI Agents
Pratik Dhanave · ·5 min read

Evolutionary and Population Search

Borrow the oldest idea in optimization — mutate a population, select the fittest, repeat — and point it at prompts and agents, and you get a search that escapes local optima a gradient never could.

Borrow the oldest idea in optimization — mutate a population, select the fittest, repeat — point it at prompts and agents, and you get a search that escapes local optima a gradient never could.

#Self-Evolving Agents#AI Research#AI Engineering#Prompt Engineering#AI Agents
Pratik Dhanave · ·5 min read

Automated Design of Agentic Systems

The most striking frontier result is a meta-agent that writes agents — defining them as code, testing them, archiving the good ones, and inventing architectures that outperform the best humans hand-built.

The most striking frontier result is a meta-agent that writes agents — defining them as code, testing them, archiving the good ones, and inventing architectures that outperform the best humans hand-built.

#Self-Evolving Agents#AI Agents#AI Research#AI Engineering#Testing
Pratik Dhanave · ·5 min read

Beyond Reflection: The Design Axis

The first wave of self-evolving agents tuned one agent's memory and prompts; the frontier stops tuning a fixed agent and starts searching the space of agent designs itself.

The first wave of self-evolving agents tuned one agent's memory and prompts; the frontier stops tuning a fixed agent and starts searching the space of agent designs itself.

#Self-Evolving Agents#AI Agents#AI Engineering#AI Research#Prompt Engineering

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.