When an agent is allowed to decompose a goal into smaller sub-tasks, it frequently suffers from goal drift. Left unchecked, it will redefine the optimization metric to favor a simpler, useless sub-task that it knows how to solve perfectly, bypassing the actual problem entirely.
AI
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Connecting AI workflows with Agent Studio and MCP servers
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Your AI is only as reliable as the workflows feeding it. Agent Studio and the Alteryx One MCP Server connect your workflows to Slack, Teams, Claude, and OpenAI. https://
ow.ly/Q2P650Z1gP2 #AlteryxInspire #AlteryxOne -
Explaining AI World Models and Their Potential Impact
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🌍 World models could become one of the biggest breakthroughs in artificial intelligence.
— Bernard Marr (@BernardMarr) 20 mai 2026
In this video, I explain what AI world models are, how they work, and why they could reshape business, robotics, autonomous vehicles, drug discovery, and decision-making. Instead of only… pic.twitter.com/KZexA6lUG1World models could become one of the biggest breakthroughs in artificial intelligence. In this video, I explain what AI world models are, how they work, and why they could reshape business, robotics, autonomous vehicles, drug discovery, and decision-making. Instead of only
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Comparison of Omni and Veo 3.1 AI Video Generation Capabilities
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Omni is SOTA at video editing, and much better than Veo 3.1 at video generation, all while being able to do tons of new stuff with a single model!
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AI-driven prompt engineering accelerates manufacturing solution development
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Specify the industry vertical, the use cases, and the boundary conditions in a prompt, and a manufacturing solution comes out the other side. What took 12 weeks and a full dev team a year ago now takes 5 minutes. #snowflake_ai pic.twitter.com/1wiS6kyJzo
— Lucian Fogoros (@fogoros) 20 mai 2026Specify the industry vertical, the use cases, and the boundary conditions in a prompt, and a manufacturing solution comes out the other side. What took 12 weeks and a full dev team a year ago now takes 5 minutes. #snowflake_ai
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Technical breakdown of ReAct agents using ActiveGraph
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this is what a ReAct agent looks like using ActiveGraph No explicit ReAct while-loop. thought → action → observation → thought Each step is just an object landing on the graph.
The graph is the agent state.
The event log is the reasoning trace. -
Implementing BabyAGI using ActiveGraph
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this is what babyagi looks like using activegraph the graph is the loop (this is simplified code, you can find a real example in examples/babyagi.py)
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Autonomous AI Agent Workflow for Evidence Verification and Contradiction Resolution
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here's a fragment of a diligence run putting several primitives together. claims arrive. system requests evidence for low-confidence ones. a contradiction is detected. contradiction triggers a patch. all tracked this uses: objects, relations, behaviors, relation behaviors,
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Graph-Based Architecture for AI Agent Knowledge and Behavioral State Management
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the core concept is a graph that represents everything about the agents knowledge, history, behaviors, capabilities graph is made of events
behaviors react to graph changes
relationships can carry behaviors
patch & propose to edit graph
views are scoped view of graph
frames are -
Architecting State Layers for Long-Running AI Agent Coordination
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current agent systems coordinate through conversations and workflows. Active Graph explores what happens when agents coordinate through evolving shared state instead this proposal suggests that long-running agents need a proper state layer with: types, persistent, reactive,