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How to evaluate AI SRE agents for production?

Hemant Kumar

Staff SRE @One2N

AI SRE agents promise faster incident response. The hard part is knowing which ones you can trust when production is noisy. This post shares how we evaluated OpenSRE before we let any of them influence a production incident.

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Git was not built for multiple AI agents. Worktrees fix that.

Srivatsa RV

SRE @One2N

Spandan Ghosh

Content @One2N

Running multiple AI coding agents on the same repo? Git's single-checkout model breaks fast. Learn how git worktrees give each agent its own isolated workspace.

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Exploring Retrieval Augmented Generation (RAG): enhancing LLMs with external knowledge

Harsh Mishra

SRE

Saurabh Hirani

Principal SRE @One2N

Explore how Retrieval Augmented Generation (RAG) can enhance Large Language Models (LLMs) like ChatGPT by leveraging external knowledge sources. Understand the fundamentals of LLMs, the limitations of standalone models, and the key components of a RAG-based application.

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How we used Fly.io to improve a Pipecat-AI chatbot

Ajinkya Wadekar

SRE @One2N

Spandan Ghosh

Content @One2N

Learn how we improved our AI chatbot application using innovative solutions. Find out about the challenges we faced and the strategies we used to boost performance and scalability.

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Understanding MLOps from a software engineers' perspective

Harsh Mishra

SRE

Spandan Ghosh

Content @One2N

Chinmay Naik

CEO @One2N

Jaideep Khandelwal

CTO @One2N

A comprehensive overview of MLOps for software engineers, covering key concepts, challenges, and best practices. Learn about knowledge silos, the MLOps lifecycle, automation, versioning, experiment tracking, and more.

Talks

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Token Economics, Paisa, Prompts & Production | Entain Tech Brews

Srivatsa RV

SRE @One2N

Biswa Prakash Mishra

SRE @One2N

In this Tech Brews at Entain talk, Srivatsa RV and Biswa dive deep into token economics and the art of token optimization for LLM applications. They explain the fundamental concepts of tokens, the critical differences between prefill and decode phases, and how caching strategies can drastically reduce AI inference costs. By breaking down prompts into cache reads and un-cached tokens, they reveal practical techniques for maintaining cache stability, structuring context, and optimizing dynamic data to achieve up to 90% cache hit rates.

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AI Enabled SDLC | Entain Tech Brews

Jaideep Khandelwal

CTO @One2N

We’ve all “vibe coded” with AI. In this Tech Brews at Entain talk, Jaideep Khandelwal shows how to turn that into a disciplined, AI‑enabled SDLC that teams can trust in production.

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Talking to Coding Agents from Neovim

Saurabh Hirani

Principal SRE @One2N

In this Mitramandal.ai session, Saurabh Hirani goes under the hood of how Neovim talks to CLI‑based coding agents like Claude Code, OpenCode, Pi, and Ko, and why these patterns apply to any editor you use. He traces his own journey from Neovim to VS Code and back, driven by the realization that the best agentic UX often lives on the command line rather than in editor chat sidebars.

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Solving Synthetic Data generation using LLMs | mitramadal.ai EP2 - 2026

Chinmay Naik

CEO @One2N

In this talk at mitramandal.ai EP2, Chinmay Naik (Founder & CEO, One2N) walks through how One2N solved the problem of synthetic data generation using LLMs - covering the real-world use cases that drove it, why anonymization falls short, and how they built a tool that uses LLMs for schema generation and deterministic faker-based data generation.

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The implications of AI in the SRE world | One2N Bits Ep.5

Jaideep Khandelwal

CTO @One2N

Saurabh Hirani

Principal SRE @One2N

AI in SRE is not just about flashy incident bots or code‑completion in your terminal. In this One2N Bits episode, Saurabh Hirani (Principal SRE, One2N) and Jaideep (CTO, One2N) break down what it really takes for AI to be useful in SRE teams, from documentation and RCAs to on‑call culture and leadership incentives. ​ ​They discuss whether AI can actually replace SREs, how to think in terms of “sub‑agents” mapped to SRE workflows, and why organizational context, knowledge sharing, and responsibility ownership matter more than yet another AI tool. ​ What you’ll learn Why AI in SRE is more than a code completion agent, and how to scope AI sub‑agents across incident management, capacity planning, and automation. How to handle knowledge hoarding, fear of replacement, and responsibility diffusion when AI is mandated from the top. Practical ways to use AI to clean up RCAs, keep architecture and docs fresh, and shorten onboarding feedback loops for SREs. How AI can free SRE teams from boilerplate and cognitive load so they can focus on business‑critical reliability work.

Watch Talk

Using LLMs to Improve Incident Response Times | mitramadal.ai - 2025

Jaideep Khandelwal

CTO @One2N

mitramandal is an AI meetup by One2N where engineers share how they've actually integrated AI into their daily development workflows. every developer is solving applied AI differently: some are orchestrating agentic pipelines across MCPs, others are wiring real-time RAG loops into Cursor or Claude, and more are operationalizing multimodal copilots inside CI/CD. we've seen fine-tuning flywheels, retrieval sandboxes, and autonomous evaluation harnesses that ship to prod every week. Talk 1: Using LLMs to Improve Incident Response Times - Jaideep Khandelwal : https://youtu.be/f_nV4GyWO0k?si=Zt_PnLzongzt4E9q Talk 2: The long way to production - Aditya Kasibhatla : https://youtu.be/SWW9rEzgxvA?si=Tr_Z_hz0gAb0JAqd Talk 3: vMCP LEGO blocks for AI workflows and agents - Shiladitya Banerjee : https://youtu.be/Fi8j3NG7WYA?si=xHRJwandjrJLUo0N

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Extreme Programming in the Age of AI | One2N Cursor Meetup Pune

Nilesh D

Software Engineer @One2N

Nilesh D at the first Cursor Meetup in Pune: how Extreme Programming principles level up dev workflows and integrate with AI tools like Cursor.

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AI Enabled SRE Practices | One2N Cursor Meetup Pune

Saurabh Hirani

Principal SRE @One2N

Tune in to this session from the first official Cursor Meetup in Pune, hosted at One2N, this lightning talk is by Saurabh Hirani on what are AI enabled SRE (Site Reliability Engineering) practices.

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A SRE’s Guide to LLMOps: Deploying and Managing AI⧸ML Workloads using Kubernetes

Harsh Mishra

SRE

Harsh Mishra covers LLMOps from an SRE perspective: unique challenges, best practices, and how to future-proof cloud strategy for GenAI and LLM workloads.

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ABC of LLMOps - What does it take to run self-hosted LLMs｜Rootconf mini 2024

Jaideep Khandelwal

CTO @One2N

Lessons from deploying open-source models on Kubernetes GPUs, managing vector DBs & building RAG apps

Watch Talk

From DevOps to MLOps - Scaling ML models to 2 million API requests per day

Chinmay Naik

CEO @One2N

Learn how to deploy and scale Machine Learning models to 2 Million+ requests/day using MLOps best practices. In this talk, you’ll learn how to go from data preparation to deployment to scaling ML models that can run at large scale - all without breaking the bank.

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