🚀 Deep Agents: The Weekly Roundup 🚀 Dive into our new resources to help you build Deep Agents capable of handling complex, long-running tasks. ✏️ Context engineering is key to reliable agents. Deep agents need detailed context and prompts, and filesystems can help manage that context. We wrote up some strategies for using filesystems to improve agent reliability. Read the blog: https://lnkd.in/g2E66mzR 🤷♀️ What are Deep Agents? - We break down the key things to know when you’re building an agent to handle more complex tasks. Watch the video: https://lnkd.in/g6GdWUMC ⚽ Using Skills with Deep Agents CLI - Agent skills are now available in the Deep Agents CLI, enabling you to use the large and growing collection of public skills with your agents. Watch the video: https://lnkd.in/dKxT4Tqh & blog: https://lnkd.in/dMUHA2hf 📚 LangChain Academy Course - If you’re not sure how to get started, our free LangChain Academy course covers the four features that set Deep Agents apart: planning, file systems, sub-agents, and prompting. By the end, you'll design, implement, and deploy your own. Enroll now: https://lnkd.in/gVhdxn8W
This is a fantastic overview, LangChain, the development of deep learning agents, illustrates how rapidly agent-based AI is advancing in capability and versatility. At Corptive Research Pvt Ltd, we are enthusiastic about how these innovations can lead to smarter automation, more adaptive systems, and impactful applications across various fields. It’s evident that keeping up-to-date and experimenting with these tools is becoming crucial for innovation driven by research.
This roundup highlights essential strategies for developing Deep Agents. How do you envision the role of context engineering evolving in future applications?
Really impressed by the new agentic AI frameworks. I put together a small multi-agent research demo using Claude models and wrote a short breakdown here ->>> https://tinyurl.com/ydt3jk65
Filesystems for context engineering is a smart approach to boosting agent reliability. Intriguing. Has anyone experimented with using real-time sensor data as a dynamic context source for Deep Agents? Empowering skills #ForAll. Try ClavePrep for instant interview prep: https://tinyurl.com/claveprep and follow ClaveHR for more insights and updates: https://clavehr.in
Deep Agents are the first time “context” stopped meaning giant prompt walls for me. I have a single project file that already behaves like a Deep Agent on LinkedIn: it defines 10 tasks, a small policy layer, and a receipts protocol the model must follow. The win was not more tools, it was treating the file system as an operating system for the agent rather than a scratchpad. Curious if you have plans for first class governance primitives in Deep Agents CLI next: constraints, eval suites, and risk levels baked into the plan instead of bolted on later.