The Esperanto of AI Agents: How the Agent Data Protocol Unifies a Fragmented Ecosystem
The Problem of Fragmented Agent Intelligence Building large language model (LLM) agents has long been haunted by a quiet paradox. Despite a growing number of agent datasets—from web navigation to software engineering—researchers rarely fine-tune their models across these diverse sources. The reason is not a shortage of data, but a lack of coherence: every dataset speaks its own dialect. One uses HTML trees; another records API calls; a third logs terminal sessions. Converting them all for fine-tuning an agent is a nightmare of custom scripts, mismatched schemas, and endless validation. ...