Agentic

Karl Marx once wrote that ``the human essence is the ensemble of social relations'', suggesting that individuals are not isolated entities but are fundamentally shaped by their interactions with...

We envision a new era of AI, termed agentic organization, where agents solve complex problems by working collaboratively and concurrently, enabling outcomes beyond individual intelligence. To...

A practical guide to Agent Engineering: the intersection of software, systems and security engineering.

Design robust multi-agent AI systems in Google Cloud.

In this blog piece, you’ll learn why and how we built LangGraph for production agents—focusing on control, durability, and the core features needed to scale.

Manus approaches to context engineering.

The blog explores how to apply practical context engineering techniques using Agno to build AI agents that are faster, more efficient, and better at collaboration. It covers core techniques that include crafting precise system messages, selectively managing context to reduce token use, applying few-shot learning to teach behavior, and coordinating multi-agent teams effectively.

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Taking care of your context is the key to building successful agents. Just because there’s a 1 million token context window doesn’t mean you should fill it.

How to test agent ideas in hours using Claude Code SDK without building orchestration infrastructure - proven methodology from consulting engagements

Agentic Design Patterns A Hands-On Guide to Building Intelligent Systems, Antonio Gulli Table of Contents - total 424 pages = 1+2+1+1+4+9+103+61+34+114+74+5+4 11 Dedication, 1 page Acknowledgment, 2 pages [final, last read done] Foreword, 1 page [final, last read done] A Thought Leader's ...

Build invincible apps with Temporal's open source durable execution platform. Eliminate complexity and ship features faster. Talk to an expert today!

Deploy More Resilient Apps. Hatchet is a platform for building distributed web apps that solves scaling problems like concurrency, fairness, and rate limiting. Instead of managing your own task queue or pub/sub system, you can use Hatchet to distribute your functions between a set of workers with minimal configuration or infrastructure.

Inngest's durable functions replace queues, state management, and scheduling to enable any developer to write reliable, multi-step code faster without touching infrastructure.

Applications of reinforcement learning (RL) in AI model building has been a growing topic over the past few months. From Deepseek models incorporating RL mechanics into their training processes to...

Practical lessons from building production agentic systems

Learn why agent infrastructure is essential to handling stateful, long-running tasks — and how LangGraph Platform provides the runtime support needed to build and scale reliable agents.

This post shares the local optima Manus arrived at through our own "SGD". If you're building your own AI agent, we hope these principles help you converge faster.

Postman for MCPs - A tool for testing and debugging MCPs - GitHub - MCPJam/inspector: Postman for MCPs - A tool for testing and debugging MCPs

AI Engineer at ANZ Bank working on intelligent systems, LLM optimization, and scalable ML platforms.

Contribute to a2a-community/a2a-ui development by creating an account on GitHub.

Repo migrated to a2a-community ogranization https://github.com/a2a-community/a2a-ui - egor-baranov/a2a-ui

A2A (Agent2Agent Protocol) and ACP (Agent Communication Protocol) represent two mainstream technical approaches in AI multi-agent system communication: 'cross-platform interoperability' and 'local/edge autonomy' respectively. A2A, with its powerful cross-vendor interconnection capabilities and rich task collaboration mechanisms, has become the preferred choice for cloud-based and distributed multi-agent scenarios; while ACP, with its low-latency, local-first, cloud-independent characteristics, is suitable for privacy-sensitive, bandwidth-constrained, or edge computing environments. Both protocols have their own focus in protocol design, ecosystem construction, and standardization governance, and are expected to further converge in openness in the future. Developers are advised to choose the most suitable protocol stack based on actual business needs.

AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps. - TencentQQGYLab/AppAgent

Weights & Biases, developer tools for machine learning