[ "Production-Grade Demo Design Specification" ] α‑AGI Insight — Beyond Human Foresight [ Abstract: Objective. Deliver a complete, production-ready CLI & web demo of a multi-agent system that forecasts sectoral disruptions driven by artificial general intelligence.
AGENTS
-
Build Your VC Deck with One Prompt on DeepAgent
By
–
Build your VC deck with one prompt on DeepAgent
— Abacus.AI (@abacusai) 4 juillet 2025
Here is an example pic.twitter.com/8DbavfionCBuild your VC deck with one prompt on DeepAgent Here is an example
-
Teaching AI Agents New Skills: A Practical Flow
By
–
I actually have built that flow before. Let me find the tweet. Basically, you can teach an agent a skill!
-
Support Agents Feature Already Available in Loop
By
–
That’s very easy to do, this has that already for support agents in loop
-
LangChain Named to InfraRed 100 by Redpoint Venture
By
–
We’re honored to be named to the InfraRed 100 by @Redpoint
, recognizing the top 100 companies transforming cloud infrastructure! At LangChain, we help developers build mission-critical AI applications across the entire agent development lifecycle. In great company: -
LangSmith: Unified Platform for AI App Debugging and Monitoring
By
–
Learn the basics of LangSmith LangSmith is our unified observability & evals platform where teams can debug, test, and monitor AI app performance — whether building with LangChain or not. Find failures fast with agent observability Evaluate your agent's performance
-
Anthropic Search Results Integration with LangChain Documentation
By
–
Learn more from @AnthropicAI docs:
https://
docs.anthropic.com/en/docs/build-
with-claude/search-results
… LangChain Python docs:
https://
python.langchain.com/docs/integrati
ons/chat/anthropic/#in-tool-results-agentic-rag
… LangChain JS docs: -
Character AI’s New CEO Focuses on Entertainment and Creator Tools
By
–
"We're doubling down on entertainment, doubling down on trust and safety." Our new CEO, Karandeep Anand, sat down with @CNN to tease the next chapter for @character_ai
. In his first 60 days, he's focused on: Smarter tools for creators A richer social feed experience -
Task B: Summarize Text Using Keywords and Agents
By
–
Define Task B: Summarize based on Task A
summary = http://
pypilot.run( f"Summarize the text using only these keywords: {keywords}", agents=[agent_b], result_type=str, context={'text': long_text}
) -
Task Orchestration Pipelines for Real-World AI Applications
By
–
This ensures Task B only runs after Task A completes—creating clean, interpretable, and robust pipelines. Perfect for real-world use-cases: Keyword-based summarization Sentiment-guided user feedback Topic-powered content generation Context-aware chatbots