Surya Prakash Jayanti

This week's AI pulse: Agentic AI is rapidly expanding its capabilities, from open-source coding assistants and accelerated security exploit discovery to hybrid compute strategies and new model iterations, reshaping how technology professionals build and secure systems. 1. Know this Automated Agents Accelerate Security Exploit Discovery Why it matters: Modern AI coding agents are proving exceptionally effective at identifying vulnerabilities. Security patches shared publicly are now attracting automated exploit probes within minutes, drastically reducing the window for organizations to deploy fixes before being targeted. This trend demands an immediate re-evaluation of security response protocols. Action: Implement proactive monitoring of public repositories for early patch discussions and consider automated deployment pipelines for critical security updates to minimize exposure time. Foster a culture of "exploit-aware" development. 2. Try this AWS Open Sources ...

This week's AI pulse: Agentic AI is moving firmly into production, demanding refined cost controls and sparking intense competition across the enterprise stack, even as core infrastructure evolves. 1. Know this Cloudflare Slashes GitHub Issues by 85% Using AI Agents Why it matters: Cloudflare's success with AI agents on Astro demonstrates tangible productivity gains and operational efficiency achievable in software development workflows, particularly for high-volume triage tasks. This highlights the growing maturity of agentic AI for practical, impactful applications. Action: Evaluate your organization's current high-volume, low-complexity operational tasks, especially in areas like issue triage or initial support, for potential automation using agentic AI solutions. Pilot a targeted project to measure efficiency gains. 2. Try this Manage AI Agent Costs with Effective Telemetry Pipelines Why it matters: As autonomous agents move from experimentation to producti...

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...