Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale

Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale

Author: Demetrios February 10, 2026 Duration: 57:24

Ereli Eran is the Founding Engineer at 7AI, where he’s focused on building and scaling the company’s agentic AI-driven cybersecurity platform — developing autonomous AI agents that triage alerts, investigate threats, enrich security data, and enable end-to-end automated security operations so human teams can focus on higher-value strategic work.


Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale // MLOps Podcast #361 with Ereli Eran, Founding Engineer at 7AI


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// Abstract

A conversation on how AI coding agents are changing the way we build and operate production systems. We explore the practical boundaries between agentic and deterministic code, strategies for shared responsibility across models, engineering teams, and customers, and how to evaluate agent performance at scale. Topics include production quality gates, safety and cost tradeoffs, managing long-tail failures, and deployment patterns that let you ship agents with confidence.


// Bio

Ereli Eran is a founding engineer at 7AI, where he builds agentic AI systems for security operations and the production infrastructure that powers them. His work spans the full stack - from designing experiment frameworks for LLM-based alert investigation to architecting secure multi-tenant systems with proper authentication boundaries. Previously, he worked in data science and software engineering roles at Stripe, VMware Carbon Black, and was an early employee of Ravelin and Normalyze.


// Related Links

Website: https://7ai.com/

Coding Agents Conference: https://luma.com/codingagents


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Connect with Ereli on LinkedIn: /erelieran/


Timestamps:

[00:00] Language Sensitivity in Reasoning

[00:25] Value of Claude Code

[01:54] AI in Security Workflows

[06:21] Agentic Systems Failures

[12:50] Progressive Disclosure in Voice Agents

[16:39] LLM vs Classic ML

[19:44] Hybrid Approach to Fraud

[25:58] Debugging with User Feedback

[33:52] Prompts as Code

[42:07] LLM Security Workflow

[45:10] Shared Memory in Security

[49:11] Common Agent Failure Modes

[53:34] Wrap up


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
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