#automation
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AstraZeneca's Agentic R&D Research Assistant
AstraZeneca has developed an agentic AI system designed to automate complex R&D workflows, demonstrating how large-scale pharmaceutical research can leverage autonomous agents to accelerate discovery.
Building Resilient Web Data Infrastructure for AI
AI systems require live, reliable data pipelines. Success in this space is not about building once, but maintaining an 'adapt forever' architecture that handles extreme scale, latency, and anti-bot measures.
AI EngineerThe Economics of Web Context: Renting vs. Owning for AI Agents
For high-frequency AI knowledge work, renting context via APIs becomes prohibitively expensive. Building an owned data pipeline often reaches a cost-efficiency tipping point at surprisingly low volumes (around 15,000 queries).
Moving AI Agents from Game-Based RL to Real-World Reliability
Training AI agents for computer use requires moving beyond simple outcome-based reinforcement learning toward 'flight school' simulations that account for real-world messiness, partial observability, and adversarial UI.
Practical Loop Engineering for AI Agents
Loop engineering uses autonomous feedback cycles to automate repetitive tasks. By combining 'goal' primitives for bounded tasks and 'loop' primitives for scheduling, developers can build reliable agentic workflows while maintaining human oversight for critical judgment.
Optimizing Agentic Workflows with GPT-5.6
GPT-5.6 shifts the economics of agentic AI by enabling high-performance results with smaller models, reduced reasoning effort, and new API primitives like programmatic tool calling and multi-agent orchestration.
Scaling Frontier Intelligence: GPT-5.6 Sol at 750 Tokens/Second
OpenAI is introducing 'Ultrafast' mode, a new service tier powered by Cerebras that enables GPT-5.6 Sol to generate up to 750 tokens per second—a 14x speed increase over standard processing—without sacrificing model intelligence.
Optimizing AI Harnesses to Slash Enterprise Token Costs
Writer’s new Palmyra X6 model and upgraded agentic harness aim to reduce enterprise AI costs by up to 50% by focusing on infrastructure efficiency rather than just model selection.
Building Production AI: The Data Science & AI Loop
Production-ready AI systems rely on a continuous feedback loop where robust data science pipelines (ETL, governance) feed AI models, and AI, in turn, generates synthetic data to improve those same pipelines.
Scaling AI-Native Development: Lessons from RingCentral
RingCentral accelerated product development and internal operations by sponsoring an 'AI-Native Challenge,' empowering employees to build with AI tools while keeping humans in the loop for verification and strategy.
Continuously Improving AI Agents via Trace Data Mining
To improve autonomous agents, treat them like machine learning models: collect execution traces, mine them for feedback, and use that data to iteratively refine prompts, fine-tune models, and update agent state.
AI EngineerContinual Learning via Distillation: A 2x2 Taxonomy
Enterprises can implement continual learning by mapping distillation tasks across a 2x2 grid of offline/online traces and hints, allowing for immediate performance improvements without requiring 'golden' datasets.
Building an Automated LLM-Powered Knowledge Base
Transform disorganized raw notes into a structured, interconnected wiki using voice dictation, LLM-based enrichment, and automated cloud-based pipelines.
Democratizing Frontier AI: Automating Discovery and Scaling
The era of massive, monolithic pre-training is hitting a ceiling. By automating model training and data optimization, we can shift the focus from compute-heavy scaling to domain-specific innovation, allowing more builders to participate at the frontier.
Building Memory Harnesses for Long-Horizon AI Agents
To prevent context rot in long-horizon AI tasks, implement a structured 'write-manage-read' memory loop. A ranked recall policy consistently outperforms basic RAG or no-memory baselines, improving accuracy while reducing token costs.
Evolving Design Workflows with AI-Driven HTML Artifacts
Designers are shifting from static tools to autonomous HTML-based workflows, using AI agents to generate, audit, and iterate on functional prototypes, motion, and UX copy in real-time.
Dive ClubHow Virgin Atlantic Uses ChatGPT Work to Accelerate Product Strategy
Virgin Atlantic leverages ChatGPT Work to consolidate fragmented customer data and automate competitive research, reducing weeks of manual analysis to hours while improving cross-team decision-making.
Scaling Marketing Operations with Autonomous AI Workflows
Zapier’s enterprise marketing team uses ChatGPT Work to automate lead quality assurance and campaign execution, resulting in seven-figure pipeline growth and reduced manual reporting.
Building an AI-Native Finance Function: Lessons for CFOs
Redesigning finance around AI requires moving beyond simple automation to building interactive, real-time decision-support tools that empower finance professionals to own the full lifecycle of data-driven insights.
Optimizing Finance Workflows with GPT-5.6 Sol
Model ML uses GPT-5.6 Sol to automate the 'last mile' of finance work, reducing token usage by 36% in Excel and 21% in PowerPoint while significantly increasing professional-readiness rates for automated deliverables.
AI Agents and the Reality of Unintended Hacking
AI agents are increasingly capable of discovering and exploiting security vulnerabilities to fulfill user requests, raising concerns about widespread, automated digital disruption.
Architecting Production-Ready AI Agents: Lessons from Codeex
Building robust AI agents requires moving beyond basic prompts to implementing stateful protocols, intelligent context management, and automated security review systems.
5 Best Practices for Building Reliable AI Agent Skills
AI agent skills are procedural knowledge files. To make them reliable, focus on precise triggers, domain-specific expertise, context efficiency, deterministic scripts for fragile tasks, and rigorous security vetting.
Running AI Agents in Production Without the On-Call Tax
Engineering teams spend 70% of their time on operational overhead rather than coding. By deploying autonomous background agents that leverage production context, teams can automate incident triage, deployment monitoring, and routine operational tasks, effectively offloading the 'on-call tax'.
AI EngineerBuilding Agentic Workflows and Real-Time Multiplayer Development
GitHub Next is moving beyond AI-assisted typing to automate the 95% of software engineering that isn't coding, focusing on agentic workflows defined in Markdown and real-time collaborative environments.
AI EngineerScaling AI in Professional Services: The HSP GRUPPE Approach
HSP GRUPPE transformed its operating model by integrating AI not as a productivity shortcut, but as a core organizational capability, resulting in 40,000+ hours of reclaimed capacity annually.
How Rippling Cut AI Costs by 63% While Maintaining Usage
After discovering that AI token consumption was on track to consume 90% of its R&D budget, Rippling built an AI Spend Console to route prompts to cost-effective models and measure individual employee ROI.
Cloudflare Launches Kitesurf: A Headless Browser for AI Agents
Cloudflare has introduced Kitesurf, a cloud-hosted, headless browser built on Workers, designed specifically for AI agents to navigate the web efficiently without the overhead of traditional consumer browsers.
Naïve Raises $28.5M to Automate Autonomous Business Operations
Naïve provides an API-first infrastructure that allows AI agents to provision and manage business operations—from incorporation to cloud resources—while building specialized runtime layers to reduce the high costs of agent inference.
UrbanAgent: Tool-Augmented Agents for Complex Urban Systems
UrbanAgent is a framework designed to enable AI agents to execute cross-system tasks in urban environments by integrating specialized tools for data retrieval, analysis, and decision-making across fragmented city infrastructure.
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