Before AI Hits Production, We Try to Break It At ICS, AI never gets near production until it’s been stress-tested. Our teams: - Engineer adversarial prompts - Test for bias and regulatory blind spots - Document failures with humans in the loop at every step If you’re deploying AI in a regulated environment, request an ICS AI Red Team working session: https://zurl.co/OMxLi #AIGovernance #AICompliance #HumanInTheLoop #RedTeam
ICS AI Red Team: Stress-Testing AI Before Production
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The technology worked in every case. Nobody asked whose intent it was serving. 90 seconds on the structural failure pattern hiding inside AI deployments at every scale — and what changes when the evaluation question comes first. 🔗 to the full article and the live experience in comments. #ArtificialIntelligence #AIStrategy #AICompanion #IntentDrivenAI #TheMobileEraOfIntent #MEIWeekly
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Frinks AI replaces inconsistency with machine-driven precision. Hear Dharmgya Sharma, Co-founder of Frinks AI, explain how their visual inspection models eliminate human error across large-scale production lines. He shares how their hardware-agnostic tech handles extreme speeds, up to 3 meters per second, ensuring high throughput without latency bottlenecks. Watch the full video here - https://lnkd.in/gnVjph53 #MAP2025 #JioGenNext #JioGenNextAcademy #Reliance #FounderTalks
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In multi-agent AI systems, error amplification is multiplicative. Each additional agent in a chain increases fragility, and most failures are invisible by default. Fallback chains are a practical response, circuit breakers adapted for AI, and treating the coordinator as your primary resilience surface. Latest Thinkata Insight 👇 https://lnkd.in/gbZ6qEkV #AIArchitecture #CompoundAI #LLM
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Learn how to add short‑term memory to your AI agent. Annie breaks down sessions, events, and state so your agent can retain context and pass information between tasks. #ShortTermMemory #AIAgents #ADK #ContextManagement #AgentDevelopment
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Most AI agents have amnesia. They forget what happened 5 minutes ago. They repeat mistakes. They lose context mid-conversation. We built Snipara to fix this. → Persistent memory across sessions → Context optimization — fewer tokens, better answers → Multi-agent coordination — 10+ agents sharing one brain We run it in production every day at Starbox Group. Our agents remember decisions, avoid hallucinations on critical data, and work as a real team. The difference between a demo and production AI? Memory. snipara.com #AI #AgentMemory #LLM #ContextOptimization #SwissTech
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