Claude Opus 4.7 is now available on Poe. Anthropic's most capable model yet, with major improvements in coding, reasoning, long-context understanding, tool use, vision, and multi-step agent workflows. You can try it in Poe app on all platforms and in the Poe API at
AGENTS
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Enterprise AI Leaders Adopt Decision Ownership and Agent Orchestration
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Enterprise AI leaders are shifting to: → Decision ownership layers → Execution guardrails → Agent orchestration → Context-driven data systems
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Full Architecture for Agentic AI Systems Unveiled
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Want the full architecture?
Comment “AGENTIC” or DM. -
Decision Systems: The Better Alternative to Current Approaches
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What works instead:
Decision systems. -
Claude Opus 4.7 Features Async Work Image Handling UI Improvements
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Some of my favorite things in Opus 4.7:
– Very good at async work and following instructions
– Effort levels are far more predictable for token control (+ new xhigh level)
– No more downscaling of high-res images
– Noticeably more taste in UIs, slides, docs -
8 Skills to Manage the New AI-Agent Workforce
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8 Skills You Need to Manage the New AI-Agent Workforce Managing AI agents requires new leadership skills — from oversight to collaboration with intelligent systems. Read more https://
bernardmarr.com/8-skills-you-n
eed-to-manage-the-new-ai-agent-workforce/
… #AI #Leadership #FutureSkills #BernardMarr -
Anthropic launches Claude Opus 4.7 with improvements
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Anthropic released Claude Opus 4.7 Opus 4.7 is a notable improvement over Opus 4.6 in software engineering and vision tasks. > Opus 4.7 handles complex, long-running tasks with rigor and consistency, pays precise attention to instructions, and devises ways to verify its own
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Spring AI SDK for Amazon Bedrock AgentCore Launch
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Spring AI SDK for Amazon Bedrock AgentCore! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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Agent Evals Drift from Production Reality Standards
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Agent evals are drifting away from production reality. Most benchmarks use clean tasks, well-specified requirements, deterministic metrics, and retrospective curation. Production work is messier, with implicit constraints, fragmented multimodal inputs, undeclared domain
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Agents AI autonomous workflows eliminating manual human UI interactions
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I am getting radicalised against human only UIs. Let my agents cook and don't make me press another button ever again.