Agentic systems, tool use, orchestration, and automation.
27 articles
This topic is about building operational agents, not demos.
Tool contracts, failure modes, guardrails, and observability matter more than clever prompts.
Why agentic AI failures create governance and operational control risks beyond model accuracy alone.
A look at how mcp-proto-okn connects natural language to scientific knowledge graph queries and reproducible workflows.
View LLM agents as runtime-adaptive computation graphs to optimize accuracy, cost, latency, debugging, and control.
Examines AI exposure in clerical work, automation pressure, and why task redesign and human accountability matter.
Defines skills as executable function code and manages them online via create-run-update-on-fail-save-on-success loops.
Move beyond context/output limits: evaluate LLM code integration with task decomposition, tool parity, and reproducible build/test rubrics.
A practical look at memory admission control for LLM agents, reducing long-term memory pollution while improving auditability and metrics.
How AI automation turns speed into new baselines, raising pressure, and how to redesign sustainable standards using risk-based governance.
Even with the same model alias, outputs can shift due to snapshot routing, safety behaviors, and sampling settings. Use logs and regression tests to isolate causes.
Tool calls become real actions. JSON validity is not enough—use strict schema checks, allowed_tools, refusal detection, and state-aware gates.
AI coding tool choice depends on not only model quality but also tool calling, agents, and permission design shaping security and team velocity.
Analysis of 2026 AI agents transitioning to autonomous execution using CUA and state-based graph structures.
A field report from running a community bot: what automation can do, and what still requires human operational control.
LFM2 series enables high-performance local AI on low-memory devices using hybrid architecture and Model Context Protocol.
Explore how the Model Context Protocol (MCP) standardizes data integration for AI agents and resolves data silos in business workflows.
Explore strategic workflows using Anthropic's MCP and DeepSeek's CoT to transform AI into proactive coding agents.
Explores building welfare systems with digital IDs to address AI labor displacement while ensuring social inclusion for all.
Anthropic introduces interactive app features in Claude, enabling direct control of external tools like Slack via MCP.
Claude Cowork automates file management and system tasks through a user-friendly interface for enhanced workflow productivity.
Analyze how OpenAI's Responses API and MCP reduce AI agent latency and improve cache efficiency through server-side state management.
Analyzes the shift to AI-driven 24/7 systems and its impact on labor productivity, income distribution, and automation.
Why Open Responses exists, what it standardizes beyond Chat Completions, and how a self-hosted drop-in server fits into the picture.
35 major European banks plan to eliminate 10% of their workforce - about 200,000 jobs - by 2030 through AI adoption. Back-office, risk management, and compliance face the deepest cuts.
Anthropic's Model Context Protocol (MCP) has been donated to the Linux Foundation, becoming the official standard for AI agents. Eight big tech companies including OpenAI, Google, Microsoft, and AWS join as Platinum members of the Agentic AI Foundation.
