Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
Palantir CEO Alex Karp argues at AIPCon that enterprises must build AI-enhanced infrastructure tailored to their unique domain knowledge, or risk being hollowed out; general AI is a trap, while specialization creates moats, and cross-scenario practice transfer drives real value.
Tencent's PCG Big Data Platform built digital employees that automate data analysis tasks like weekly reports, reducing delivery from days to minutes through a modular agent architecture combining LLM scheduling, tool protocols, semantic knowledge, memory systems, and evaluation-driven development with three key metrics.
AWS open-sourced Strands Harness, a pre-assembled agent runtime that decouples the model from the execution system; benchmarks show using the same Claude Fable 5 model, Strands achieves 77% lower cost and higher accuracy than Claude Code by optimizing context management, prompt caching, and layered memory, proving harness design critically impacts agent performance.
Amap, JD Retail, and Xiaoying Technology share production architectures for marketing ROI evaluation, conversational analytics, and voice agents — all converging on making AI recognize its own epistemic limits rather than just chasing accuracy.
Google's BigQuery Graph with Measures integrates governed metrics with property graphs to give AI agents both accurate calculations and traversable business relationships, enabling root-cause analysis beyond simple metric queries, with zero-ETL mapping from existing tables and Looker integration for unified semantic models.
At Palantir AIPCon 11, Eaton demonstrated Equipment Forge, a multi-agent system that uses Palantir's Ontology to coordinate engineering requirements, bill-of-materials checks, supplier lead times, and delivery commitments for custom electrical equipment, keeping humans in the loop for critical decisions while automating cross-domain analysis.
The article explores the critical architectural choice between semantic mapping and materialized object storage for ontology implementation, detailing Tianyi Credit's lessons learned, a hybrid approach with core objects materialized, consistency via incremental updates, and a measurable delivery template.
Li Auto engineers reveal that production AI Agent failures stem from fragmented context rather than model limits, detailing their ontology-driven four-layer architecture and three architecture iterations that transform context into governable, traceable semantic assets for stable delivery.
Utopia introduces an open-source Enterprise World Model that replaces static RAG with a bitemporal knowledge graph tracking when facts were true and when systems knew them, adding conflict resolution, ontology-driven entity resolution, and MCP integration so agents query versioned business states rather than just latest documents.
EvoOntology introduces a self-evolving ontology layer for data agents that transforms static semantic layers into a runtime service, using a three-layer architecture and an evolution loop where agents' execution traces drive continuous ontology updates via builder and evolution agents with validation gates, reducing token usage and improving accuracy across benchmarks.
