16 posts tagged with "agentic engineering"

Why the Development Cycle Matters in Business​

Software development in a business environment isn’t just about writing code; it’s about aligning investment, execution, and market outcomes. A well-run cycle reduces risk, saves money, and gets products into customers’ hands faster. A poorly run one can burn millions before a usable product even exists.

Questions Founders Should Ask​

• Why do so many software projects fail before launch? Could technical scoping alone cost up to $400K?
• What if an AI could handle requirements and create a full PRD in minutes?
• How much time do you waste rebuilding standard features like login or payments?
• Could a multi-agent AI keep your UI, data, and logic perfectly aligned?
• What if you could cut costs, risks, and failure rates by more than 90%?

Why the Model Context Protocol (MCP) is the USB-C Your AI Agents Have Been Waiting For​

We’re standing at the edge of a new era in software development, one driven by AI agents. These agents promise to automate complex tasks, write code, and manage entire development lifecycles. But for all their power, Large Language Models (LLMs) have a fundamental limitation: they are often trapped in a black box, disconnected from the live, real-world data that businesses run on. This isolation leads to inaccuracies, or "hallucinations," and limits their usefulness in enterprise environments.

Enter the Model Context Protocol (MCP). Introduced by Anthropic in late 2024, MCP is a groundbreaking open standard that is rapidly becoming the foundational technology for building enterprise-grade AI agents. Think of it as USB-C for AI—a universal, "plug-and-play" standard designed to solve the data isolation problem once and for all.

The world of software development is in the midst of a tectonic shift, driven by a highly competitive “arms race” among a handful of technology giants to produce the most capable foundation models for coding. This intense rivalry, far from being mere corporate spectacle, is the engine powering staggering improvements in performance, functionality, and cost-effectiveness. For developers and engineering leaders, this isn't just an interesting trend to watch from the sidelines; it's a new reality to navigate.

As we look towards 2025, the primary arena of competition is shaping up between Anthropic's Claude series and Google’s Gemini family. Each model family brings a unique set of strengths to the table, offering distinct advantages depending on the task at hand. While industry-standard benchmarks give us a snapshot of the current state of play, they don't tell the whole story.

So you scoped your MVP down. You’ve got a clear PRD, a focused user journey, and a list of Must-Have features.

Great.

Building on Part One’s look at AI’s impact on software project economics and collaboration, Part Two delves into how intentional technology choices and focused frameworks amplify these gains. By moving beyond generic tools, teams can streamline workflows and optimize AI’s support for greater productivity and innovation. As generative AI continues to reshape the software development landscape, it’s essential to address the challenges that come with integrating Large Language Models (LLMs) like GPT-4 into coding workflows. While AI accelerates development and enhances collaboration, it also introduces new complexities that require careful management. Implementing guardrails, best practices and tools that ensure code quality and maintainability, is crucial for harnessing the full potential of AI-assisted coding.

We started this Substack for builders; founders, PMs, and developers who want to move past planning and start shipping. If that’s you, follow along here 👇🏻

The software development landscape is experiencing a transformative shift due to the advent of generative AI technologies. Large Language Models (LLMs) like GPT-4 are not only changing how code is written but are also redefining the economics and collaborative dynamics of software projects. Projects that once required large teams and significant capital can now be accomplished faster and more efficiently, unlocking new opportunities for businesses of all sizes.