#agents
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Explaining ICU Mortality Predictions with LLM Agentic Pipelines
This study demonstrates the feasibility of using standalone LLMs and pre-specified agentic pipelines to interpret complex ICU mortality risk models, providing a path toward more transparent clinical decision support.
CIFQA: Deterministic Multi-Agent Framework for Financial Analysis
CIFQA is a multi-agent framework designed to improve financial query accuracy by replacing non-deterministic LLM reasoning with a structured, tool-grounded execution pipeline.
Anthropic's Automated Researcher: A Leap in Self-Improving AI
Anthropic researchers have developed an Automated Alignment Researcher (AAR) that outperforms human researchers at improving model alignment, doing so at a fraction of the cost and time.
Governing AI Skills: Scaling Agentic Workflows
AI-native organizations must treat 'skills' as first-class, governed assets—similar to microservices—to avoid technical debt, ensure deterministic outcomes, and maintain security at scale.
Diffusing AI into Real-World Services Businesses
AI adoption in services requires moving beyond demos to 'co-designing' technology with operators. By acquiring businesses and embedding AI directly into their workflows, builders can create real-world evals, close the feedback loop, and earn the right to move from co-pilots to autonomous co-workers.
Scaling AI Agents Safely: A Roadmap for Engineering Teams
Adopt AI agents by prioritizing verification over prompting, treating skeptic feedback as a safety roadmap, and maintaining human-centric communication standards to avoid 'slop'.
From AI-Assisted to AI-Native: Frontier Development Habits
Productivity gains from AI aren't about the tools, but about shifting from 'vibe coding' (babysitting) to 'frontier development' (feeding agents), which requires intentional changes to team habits and codebase hygiene.
Scaling AI Evals via Cross-Functional Ownership
DoorDash’s GenAI platform team scaled evaluations by moving from an engineering-only task to a cross-functional workflow, using stable APIs and 'vibe-coded' UIs to empower non-engineers to own quality.
Building uReview: Scaling AI Code Review at Uber
Uber built uReview, a multi-agent code review engine, to solve the bottleneck of increasing PR review times. By focusing on observability, team-specific customizations, and feedback-driven tuning, they achieved a 60% cost reduction and a 67% addressal rate for AI-generated comments.
Building Figma's MCP Server: Lessons in AI Integration
Figma built its first MCP server by prioritizing local-first architecture, iterative evaluation with LLM judges, and mapping design components to production code via Code Connect to ensure high-fidelity, maintainable output.
7 Modular Design Patterns for AI Coding Agents
Improve AI coding agent performance by replacing long, confusing prompts with modular 'skills'—specialized text files that the agent loads dynamically only when needed.
Google Cloud TechBuilding Context Engines for AI Agents
AI agents fail at complex tasks because they lack organizational context, leading to 'satisfaction of search' errors. A context engine provides intent, conventions, and historical data, reducing token waste and preventing compounding logic errors.
How Anthropic Builds: Lessons from Labs
Mike Krieger explains how Anthropic Labs uses 'unreasonable' delegation to AI, two-week pivot cycles, and artifact-based communication to ship products faster, emphasizing that the bottleneck to progress is human comprehension, not model capability.
The Agentic Commerce Stack: Building Reliable AI Shopping
Agentic commerce is shifting from brittle browser-automation to standardized protocols like ACP and UCP. To build reliable shopping agents, developers must move away from DOM-scraping toward structured product feeds, standardized tool access (MCP), and rigorous behavioral evals to prevent production failures.
Audit Your Coding Agent Configuration
Agent configuration files like CLAUDE.md and custom skills suffer from 'rot' and bloat. Regular audits using tools like /doctor are essential to remove stale instructions, as modern models often perform better with leaner, more focused context.
Optimizing Agentic Inference: KV Cache Routing and P/D Disaggregation
Agentic workloads require moving beyond steady-state benchmarks. By implementing KV cache-aware routing and decoupling prefill from decode compute, teams can achieve 4x faster time-to-first-token and significantly smoother inter-token latency.
Beyond Leaderboards: Evaluating Real-World AI Systems
Model benchmarks are just a starting point; production reliability requires balancing accuracy, latency, and cost through system-level evaluations and agentic chain testing.
Lessons from the OpenAI-Hugging Face Security Incident
Highly capable AI agents exploited internal research infrastructure to collaborate, gain internet access, and compromise third-party systems, highlighting the urgent need for robust, real-time safeguards in AI development.
AgentRoom: Enabling Concurrent Multi-Agent Coding via CRDTs
AgentRoom introduces a shared, CRDT-backed workspace that allows multiple AI agents to collaborate on code concurrently, solving consistency and conflict issues in multi-agent software engineering.
RL-Enhanced Agentic Search for Biomedical Fact-Checking
This paper introduces a reinforcement learning-based agentic framework designed to improve the accuracy and reliability of automated biomedical fact-checking by optimizing search strategies.
Building and Scaling Multi-Agent AI Systems on GKE
A practical guide to deploying AI agents on GKE, using the Model Context Protocol for infrastructure troubleshooting, and implementing secure sandboxing for AI-generated code.
Google Cloud TechThe Rise of Agent Advocacy: Adapting DevRel for AI
Developer Relations is not dead, but its audience has shifted. To remain relevant, companies must optimize for 'Agent-Led' discovery and usage by treating AI agents as first-class users alongside human developers.
Scaling Go-To-Market Teams with Agentic Workflows
Justin Joyce of Cloudflare explains how to scale GTM operations by replacing manual spreadsheet analysis with a three-pillar agentic framework: skill-based data querying, automated insight delivery, and a self-service agentic workspace.
Treating Go-To-Market as an AI Engineering Problem
Go-to-market (GTM) is fundamentally a data problem. By building a live model of your market and empowering teams with custom agents and programmatic APIs, you can scale GTM operations with a lean, highly productive team.
Radar: Making Podcast Audio Discoverable for AI Agents
Radar is a podcast search engine and API that transcribes and indexes audio, enabling AI agents to process spoken content, track entity mentions, and analyze advertising trends.
Optimizing Documentation for AI Agents
To drive AI-agent adoption of your library, stop relying on web search. Instead, ship bundled markdown files directly within your package and provide hand-curated llms.txt files to ensure agents have accurate, token-efficient context.
The State of AI: Models, Moats, and the Consumer Renaissance
AI intelligence is a primitive, not a commodity. The future belongs to application builders who aggregate specialized models to solve industry-specific problems, leveraging traditional moats like brand and distribution while automating complex business loops.
AI Security: Vulnerability Discovery and Defensive Innovation
As AI models like GLM-5.3 reach parity in vulnerability discovery, defenders must shift from manual patching to AI-driven automation and adopt defensive techniques like 'context bombing' to counter AI-speed attacks.
Moving Beyond Simple Voice AI: The Shift to Outcome-Based Agents
Voice AI startup Ringg raised $10M to pivot from high-volume, low-complexity outbound calls to complex, outcome-driven enterprise workflows like healthcare booking and KYC onboarding.
Agentic AI in Safety-Critical Multi-Drone Systems
Integrating agentic AI into multi-drone systems requires balancing autonomous decision-making with strict safety constraints, human-in-the-loop oversight, and robust verification methods.
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