Ralph Eric Kunz

Software systems work best when they’re connected to each other. For years, incumbents use deep integrations as a competitive moat. But AI upends this dynamic. A few of our portfolio companies are starting to develop integrations with AI in a matter of hours, completely upending the two or three quarter timeframes of classic enterprise integration development. This enables two important impacts to the sales cycle. First, the integration a customer desires can be built during the sales cycle, demonstrating the startup’s technical agility. And second, within a few quarters, a startup can have a vast number of integrations demonstrating breadth, establishing credibility & nullifying sales objections. Developers working with coding agents drafting a PRD, developing integration tests, and then asking the AI to iterate until it succeeds. In the medium term, companies that are able to use AI to write integrations are better equipped for agent-to-agent communications. If one AI can develop an integration into another AI, well, then those two systems can talk together seamlessly irrespective of changes within API specification, network communication challenges, state management issues, historical problems that have plagued integration. This sets the startups up well for the next generation of protocols, including model context protocol and agent2agent. AI generation of those interfaces further separates startups from incumbents. With fast integrations, startups can shorten sales cycles and be among the first to build and deploy a complete AI architecture.

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Prompt engineering is the new consulting superpower. Most haven't realized it yet. Over the last couple of days, I reviewed the latest guides by Google, Anthropic and OpenAI. Some of the key recommendations to improve output: → Being very specific about expertise levels requested → Using structured instructions or meta prompts → Explicitly referencing project documents in the prompt → Asking the model to "think step by step" Based on the guides, here are four ways to immediately level up your prompting skill set as a consultant: 1. Define the expert persona precisely "You're a specialist with 15 years in retail supply chain optimization who has worked with Target and Walmart." Why it matters: The model draws from deeper technical patterns, not just general concepts. 2. Structure the deliverable explicitly "Provide 3 key insights, their implications and then support each with data-driven evidence." Why it matters: This gives me structured material that needs minimal editing. 3. Set distinctive success parameters "Focus on operational inefficiencies that competitors typically overlook." Why it matters: You push the model beyond obvious answers to genuine competitive insights. 4. Establish the decision context "This is for a CEO with a risk-averse investor applying pressure to improve their gross margins." Why it matters: The recommendations align with stakeholder realities and urgency. The above were the main takeaways I took from the guides which I found helpful. When you run these prompts versus generic statements, you will see a massive difference in quality and relevance. Bonus tips which are working for me: → Create prompt templates using the four elements → Test different expert personas against the same problem (I regularly use "Senior McKinsey partner" to counter my position detecting gaps in my thinking.) → Ask the model to identify contradictions or gaps in the data before finalizing any recommendations. We’re only scratching the surface of what these “intelligence partners” can offer. Getting better at prompting may be one of the most asymmetric skill opportunities all of us have today. Share your favourite prompting tip below! P.S Was this post helpful? Should I share one post per week on how I’m improving my AI-related skills?

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Agent Orchestration vs Model Context Protocol (MCP) Related to Anthropic's MCP and my recent investment in an agentic AI company with cutting edge orchestration capability. (out of my curiosity) In the rapidly evolving landscape of Large Language Models (LLMs) and Generative AI (GenAI), two approaches have emerged to address the challenge of extending AI capabilities beyond simple prompts and responses: Agent Orchestration and Model Context Protocol (MCP). Both aim to enhance the functionality of AI systems, but they take fundamentally different approaches to achieve this goal. The future of Agent Orchestration likely involves: More sophisticated coordination mechanisms Better handling of agent conflicts and reconciliation Integration with emerging standards like MCP More specialized agents with deeper domain expertise The MCP ecosystem is likely to develop with: A growing library of standardized tools and integrations Broader adoption across different AI platforms Enhanced security and compliance features Extensions to handle more complex interactions So, solution vs. tool. Maybe the choice is upon the user's characteristics. (End customer vs Developer) https://lnkd.in/ghgAXKDn #MCP #Anthropic #AI #Orchestration #Efficiency #User #VC #SiliconValley

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