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newsAWS Machine LearningTrust 88 · LabPublished 12d agoLive · 10d ago

Scaling agentic AI: Enterprise patterns without vendor lock-in

Scaling agentic AI across an enterprise requires patterns that preserve flexibility while avoiding vendor lock-in. In this second post of our multi-agent series, we examine how ML teams operate many agentic AI systems across a multi-everything environment of frameworks, models, and providers, and the principles that let those systems scale together.

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PossiblePossibly related (embedding) · 62%AgentSwarms-fyi/agentswarms →
PossiblePossibly related (embedding) · 62%markus-global/markus →
PossiblePossibly related (embedding) · 61%antgroup/agentic-ai-landscape →
PossiblePossibly related (embedding) · 61%mckinsey/agents-at-scale-ark →
PossiblePossibly related (embedding) · 61%Team-Commonly/commonly →
PossiblePossibly related (embedding) · 58%StarHarness: Evolving Harnesses with Stratified Search for Enterprise Environments →

Covers

repoAgentSwarms-fyi/agentswarms repomarkus-global/markus repoantgroup/agentic-ai-landscape repomckinsey/agents-at-scale-ark repoTeam-Commonly/commonly

Covers (incoming)

paperStarHarness: Evolving Harnesses with Stratified Search for Enterprise Environments

Related across the graph

repomarkus-global/markus repoAgentSwarms-fyi/agentswarms repoTeam-Commonly/commonly repomckinsey/agents-at-scale-ark repoantgroup/agentic-ai-landscape paperStarHarness: Evolving Harnesses with Stratified Search for Enterprise Environments