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Product thinking, implementation patterns, and field lessons from building AI systems that have to run inside real organizations.
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Enterprise leaders are struggling with AI adoption right now. They face a multitude of options, frameworks, and governance challenges. Flo Boymond, CEO of Stackmint, sat down with Matthew Skelton, co-author of the highly influential book Team Topologies, CEO at Conflux, and the originator of Bounded™, the AI-native operating model toolkit, to explore the issues.
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A practical 7-step guide to becoming an AI-native agency: audit your workflows, sell outcomes instead of hours, productize AI services, add governance, and build recurring revenue.
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Generative AI is changing in nature: it no longer just answers, it begins to act. The real question is no longer the prompt, nor even the model, but the governance of execution. This is generative AI's real turning point: from the autonomous agent to governed execution.
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We are open-sourcing the Stackmint Gateway: a framework-agnostic Python client that wraps your LangChain agents to enforce strict budgets, Human-in-the-Loop (HITL) gates, and circuit breakers locally.
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As enterprises accelerate AI adoption, many are allowing AI systems to interact directly with critical business applications. Without governance layers, this creates security, compliance, and operational risks. The solution is to establish controlled execution frameworks that ensure AI actions remain safe, auditable, and aligned with business policies.
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The real AI-native agency model is not reselling chatbots. Stackmint turns repeatable expertise into governed, white-labeled capabilities and recurring revenue.
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LLM cost optimization starts with architecture. Learn why tightly scoped, governed AI workflows can reduce token consumption, prevent runaway agent costs, and lower unnecessary AI compute.
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Agentic AI is moving from demos to production. The hard part is not generating answers—it is running actions safely across real systems with auditability, policy, and control.
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