Dedicated to sharing and discussing big data and AI technology applications, aiming to empower a million data scientists. Regularly hosts live tech talks and curates articles on big data, recommendation/search algorithms, advertising algorithms, NLP, intelligent risk control, autonomous driving, and machine learning/deep learning.
The article analyzes Semantica’s open‑source “Palantir” project, which reached GitHub Trending #1, and explains how its Ontology layer extends agents beyond question answering to decision‑making and execution by modeling business objects, relationships, rules, and actions.
Palantir’s new Object Timeline feature moves AI observability from model‑level traces to business‑object lifecycles, exposing token usage, runtime, waiting time and Agentic Coverage for each object, allowing enterprises to quantify how much work agents actually perform, where bottlenecks lie, and why simple automation percentages can be misleading.
Agentic Search 2.0 expands a conversational search agent into a production‑grade AI system that orchestrates multi‑source retrieval, tool execution, memory management, and secure sandboxing to complete end‑to‑end enterprise tasks, illustrated with a consumer‑electronics research case study and concrete performance numbers.
The article breaks down five concrete design patterns in DeepSeek Harness—session logs as truth, repeat‑tool reminders, output retention, Code Mode governance, and Workspace for long‑term state—showing how the framework shifts agent development from pure model tricks to a full‑featured runtime system.
Palantir’s new Object Timeline feature aggregates token usage, runtime, waiting time and Agentic Coverage into a single view, letting enterprises quantify how much work AI agents actually perform, identify bottlenecks, and evaluate automation impact beyond traditional model benchmarks.
Around 80% of enterprise knowledge resides in unstructured documents, and extracting accurate, structured information from complex layouts, tables, and multimodal content remains a low‑accuracy, engineering‑heavy hurdle that can cripple downstream RAG, knowledge‑base, and agent deployments.
The Twin paper shows that fixing the base GPT‑5.6 model while swapping different harnesses can boost performance from 7.8 to 93.3, proving that an Agent’s true capability now depends on the combined system of model and harness rather than the model alone.
Real‑time agents face a three‑way conflict—low‑latency interaction, slow reasoning, and continuous perception—so Google’s AMIE (Video) replaces a single loop with three asynchronous agents (Talker, Planner, Perception), cutting average latency from 21.4 s to 2.6 s while preserving task performance.
The article presents a six‑layer maturity framework for large‑model AI in finance, explains how institutions can internalise generic models into knowledge, data, skills and decision systems, and shows how to evaluate technical depth, business value and risk for each layer.
The article explains that an Agent Harness— the full software infrastructure surrounding an LLM— is essential for production‑grade AI agents, detailing its definition, three engineering layers, twelve concrete components, execution loops, framework implementations, and key design decisions that separate harness failures from model shortcomings.
