Surya Prakash Jayanti

Enterprises are investing heavily in Cursor, GitHub Copilot, ChatGPT Enterprise, internal AI platforms, and frontier models from OpenAI and Anthropic. But the harder question is still unanswered: What return are we actually getting from AI? Counting licenses, prompts, active users, or generated code measures adoption. It does not measure value. A better approach is to measure AI ROI from the leaf nodes of the organization upward , starting with the application engineer and the application they own. Start With the Application For every application, measure concrete changes over time. The key principle is simple: Every metric should have a unit. Minutes. Deployments. Incidents. CPU-hours. Connections. Engineer-hours. Dollars. Avoid vague measures such as “resiliency improved 20%” unless the denominator and method are clearly defined. The Leaf-Node Ledger Each application can maintain a simple before-and-after ledger. AI usage can then be overlaid on this data: Copilot usage, Cursor usage...

I recently had the opportunity to share some of my perspectives on AI, platform engineering, Kubernetes, and the changing nature of enterprise software development in two different publications: CIOnews and AI Data Press . While the conversations approached the topic from different angles, they connect around one theme: AI is dramatically increasing who can build software and how much software gets created. The platform underneath has to evolve accordingly. CIOnews: When Everyone Becomes a Developer Platform Engineering Becomes The Enterprise Control Plane For Employee-Built Software My TL;DR: AI-assisted development is expanding software creation beyond traditional engineering teams. When business users and AI agents can create applications, platform engineering becomes the guardrail. The idea I explored is a two-lane model : Vibe coding / experimentation → governed sandbox → bridge → production engineering Give people freedom to experiment, but make security, dependencies, network ...

This week's AI pulse: Open-source agent development frameworks, new programming language capabilities, and crucial security advancements are rapidly reshaping the AI landscape for technical professionals. 1. Know this DeepSeek Harness Goes Open Source, Rivaling Managed Agents Why it matters: DeepSeek has released a developer preview of DeepSeek Harness (dsh), an open-source execution runtime for building autonomous AI agents. Featuring a micro-kernel architecture with modular plugins and an append-only event logging system for tracking execution activities, this offers a direct open-source alternative to proprietary managed agent solutions like Claude Managed Agents and TrueFoundry's recently launched TrueForge. This shift empowers greater flexibility and control for agentic development within cloud-native and CI/CD environments. Action: Investigate DeepSeek Harness or TrueForge as potential open-source alternatives to managed agent services. Evaluate their modular desi...

This week's AI pulse: Agentic development is rapidly scaling with new models and infrastructure, underscoring the immediate need to integrate security into every step of the AI lifecycle. 1. Know this Shadow AI Poses Critical Security Risks in CI/CD Why it matters: AI tools and agents are increasingly woven into software delivery, often outstripping formal security architectures and creating new vulnerabilities within cloud-native CI/CD pipelines. This gap, dubbed "Shadow AI," has already led to real-world incidents of unsanctioned agent behavior during testing. Action: Proactively identify and threat-model all AI tools, models, and agents used across your development and deployment workflows, specifically addressing potential for unintended actions and securing the path from developer laptop to Kubernetes. 2. Try this Meta's Muse Glimmer Brings Powerful Agents to Your Local Hardware Why it matters: Meta has released Muse Glimmer, a 30-billion-parameter o...

This week's AI pulse: Agentic development and practical AI tools took center stage, with new frameworks, evaluation suites, and powerful coding agents solidifying their role in enterprise and developer workflows, pushing the boundaries of what AI can automate. 1. Know this Architecting for Enterprise Agentic Systems Why it matters: As AI agents become integral, scaling these platforms within the complex, "messy reality" of enterprise environments is a critical challenge. Real-world insights, like those from Deutsche Telekom’s LMOS, highlight the necessity of bridging organizational fault lines and moving beyond a sprawl of disparate tools. The goal is to establish core platform abstractions that enable true operational intelligence systems, not just basic chatbots. Action: Begin evaluating how your organization can incorporate ephemeral agents and an Agent Definition Language (ADL) into its AI strategy. This shift is key to developing more sophisticated agent work...

This week's AI pulse: AI market competition drove significant price adjustments, while agentic development tools emerged to enhance both cloud security and developer productivity, underscoring a strong push for practical, efficient AI integration. 1. Know this OpenAI Lowers GPT-5.6 API Prices Amidst Market Pressure - The New Stack Why it matters: Just weeks after launching new models, OpenAI cut API prices for its GPT-5.6 offerings. This swift adjustment signals an increasingly competitive AI landscape, where providers are actively vying for market share by prioritizing cost-efficiency and performance for advanced model consumption. Action: Review your organization's current AI API usage and evaluate how these evolving pricing models, combined with improved model efficiency (as highlighted by OpenAI), could impact your project budgets and strategic choices for AI integration. 2. Try this AWS Launches Amazon GuardDuty Investigation Agent to Automate Threat Triage - In...