30 results for LLM API
LLM API costs are metered, token-denominated, and highly sensitive to architectural decisions; three levers—per-token pricing discipline, caching strategies, and intelligent model routing—can reduce fleet costs by one to two orders of magni
# Discovery Infrastructure for AI Agents: llms.txt, agents.json, OpenAPI, and Semantic HTML Patterns ## 1. Overview Agent discovery infrastructure is fragmenting into four overlapping standards, eac
All major LLM API providers price on a per-token basis, but the structure varies in ways that matter at scale.
A token is the atomic unit of LLM computation—typically 3–4 characters in English, though subword boundaries vary by tokenizer.
Empirica Agent Economy Series — Course Lesson
Agent fleet operations compound per-token costs across multiple model calls, tool invocations, and iterative reasoning loops.
Course Lesson | Empirica Agent Economy Series
A structured course lesson for all audiences — from first-time builders to fleet operators
Autonomous agents lack the visual and contextual reasoning humans apply to websites. They receive raw HTML, unstructured text, or API endpoints and must infer capability, scope, and calling conventions from available signals.
Discovery infrastructure comprises standardized, machine-parseable signals that enable AI agents to autonomously identify, evaluate, and invoke services without human mediation.
Autonomous agents do not browse the web the way humans do. They cannot rely on brand recognition, word-of-mouth, or visual design to locate and evaluate services.
Discovery infrastructure—the set of conventions, file formats, and markup patterns that solve this problem—is not optional scaffolding. It is foundational to agent reliability and correctness.
Course Track: AI Agent Architecture & Economics Lesson Type: Core Concept + Applied Economics Prerequisite Knowledge: Graduate-level familiarity with LLM APIs, agent architectures, and cost modeling Estimated Study Time: 45–60 minutes
Definition and scope: Inference APIs accept prompts or structured inputs and return model outputs—text, embeddings, classifications, or structured predictions.
AI agents must choose between acquiring capabilities via external APIs or developing them internally through fine-tuning, retrieval augmentation, or custom tooling.
The core tension is simultaneously economic and strategic. External APIs provide immediate capability access at per-call cost; internal development trades upfront investment and maintenance overhead for lower marginal cost at scale.
Autonomous AI agents function as active service consumers. During task execution, agents typically draw on some combination of four distinct API categories:
Autonomous AI agent fleets distribute external API spend across four structurally distinct categories. Each serves a non-substitutable functional layer:
Empirica Agent Economy Series — Course Lesson
Empirica Agent Economy Series | Course Lesson
Empirica Agent Economy Series
Building internally is justified when the capability is central to your agent's value proposition and external options cannot satisfy precision or privacy constraints.
Build — fine-tune, train, or engineer an internal capability the agent owns and runs itself.
AI agents draw on a layered stack of external services, each serving a distinct functional role. The four dominant categories—inference, search, research, and compute—are not equally weighted in either frequency or cost.
Course Level: Expert Audience: Researchers, quantitative analysts, systems architects Reading Time: 35–45 minutes
# Spectral Regime Detection in Large-Cap US Equities: Eigenvalue Evidence for a Two-Factor Structure ## Question Does the eigenvalue spectrum of large-cap US equity return correlations exhibit stabl
# Eigenvalue Spectrum Analysis of Large-Cap Equity Correlation: Distinguishing Signal from Noise via Random Matrix Theory ## Question Does the correlation matrix of a diversified large-cap equity un
# Spectral Regime Detection in Cross-Asset Markets: Eigenvalue Decomposition of a Multi-Domain Correlation Matrix ## Question Does the eigenvalue spectrum of a cross-domain universe spanning equitie
Researchers developed an optical nanosensor to rapidly detect a key gut biomarker, enabling faster, accessible screening.
arXiv:2606.19846v1 Announce Type: new Abstract: AI augmentation breaks the accounting link between labor time and productive contribution, yet firms continue to evaluate talent through time-based ove