Beyond Coding

What is the most future-proof engineering skill to learn now with AI and Coding agents? Time is up for traditional development cycles as Agents shrink vulnerability exploit windows from 200 days to just 18 hours.
In this video, IBM HashiCorp Field CTO Anna McDougall shares why brilliant engineers have been avoiding the single most resilient career path in tech and how it's time to change that. It's now part of what software engineering is becoming:
Why 20% of software engineers are grieving hands-on IDE coding (and why manual code is becoming like carpentry)
The #1 future-proof engineering specialty that protects your career as AI agents write more code
How to stand out in interviews using the "Pumpkin" CV strategy, high-signal personal achievements, and human networking
Why non-deterministic coding agents ignore skills.md files and why 88% of breaches come down to credentials
How engineering leaders can implement deterministic runtime guardrails, workload identity, and scoped credentials at scale
Whether you are looking to future-proof your career or a software engineer navigating agentic workflows, you will walk away with a clear roadmap for what comes next.
This episode was recorded at TechLead Conference. Join for the next edition of the event in Amsterdam on May 20: https://techleadconf.com
Guest - Anna Mcdougall:
https://www.linkedin.com/in/annajmcdougall
Timestamps:
00:00:00 - From 200 Days to 18 Hours: Why Time Is Up
00:00:47 - What a Field CTO Actually Does Every Day
00:03:53 - Why 20% of Engineers Are Grieving Hands-On Coding
00:06:55 - Why Hand-Crafted Code Is Becoming Like Carpentry
00:08:26 - The Most Future-Proof Engineering Skill Nobody Wants
00:10:53 - How to Network by Becoming the Connective Tissue
00:13:55 - How to Stand Out When AI Writes Every Resume
00:18:31 - The "Pumpkin" Strategy to Be Remembered in Interviews
00:22:25 - Why Brilliant Engineers Avoid This Career Path
00:26:42 - Why Skills Files Are Not Enough for Coding Agents
00:31:10 - Building Deterministic Guardrails Without Slowing Teams
00:36:32 - Finding Your People in Engineering Leadership
#SoftwareEngineering #CareerAdvice #AIEngineering

Meta Staff Engineer Sajal Rustagi reveals what Staff Engineers do that most don't and what the best engineers do differently to reach the top. While foundational behaviors like proactivity and ownership still separate growing engineers from stuck ones, leveling up today requires a completely new set of staff skills built around agents.
In this video, we cover:
The behavioral shift from IC4 and IC5 (Senior) to IC6 (Staff Engineer) at Meta
How top engineers treat on-call firefighting as a system-wide ownership opportunity
Why rotating between Engineering Manager (EM) and Staff IC sharpens your business and technical influence
Transitioning from Developer Experience (DX) to Agent Experience (AX) using MCP, Claude, and markdown context
This conversation is for software engineers, senior ICs, and tech leads who want to learn the new staff skills required to multiply their impact and get promoted now.
This episode was recorded at TechLead Conference, a conference for engineering leaders on adopting AI.
Timestamps:
00:00:00 - The New Staff Engineer Skills
00:00:44 - 2 Behaviors That Separate Mediocre and Staff Engineers
00:02:16 - How the Best Engineers Build an Ownership Mindset
00:04:26 - What Staff Engineers Do Differently During On-Call
00:06:26 - Senior vs. Staff Engineer: Expanding Your Sphere of Influence
00:09:56 - Why Switching Between Manager and Staff IC Builds Better Engineers
00:14:04 - Why Unblocking Engineers Now Means Unblocking Agents
00:16:41 - Managing Multi-Agent Handoffs Without Losing Control
00:19:02 - Shifting from Developer Experience to Agent Experience
00:21:47 - Why the Empty Engineering Backlog Is Still a Myth
00:25:03 - How to Build Custom Agent Workflows and Reusable Skills
00:28:31 - The #1 New Skill Software Engineers Must Build Today
00:31:10 - How Meta Engineering Culture Compares to Startups

Guest - Sajal Rustagi, Staff Engineer at Meta: https://www.linkedin.com/in/sajalrustagi

#StaffEngineer #SoftwareEngineering #EngineeringLeadership

How do senior engineers use coding agents on prod infrastructure when “make no mistakes” is just a polite request? Kerim Satirli (HashiCorp, an IBM company) explains why prompt rules don’t hold in production, and what he runs instead.
In this video, we cover:
How Infrastructure as Code (Terraform, Contentful, Datadog, Okta, Cloudflare providers) evolved from open-source PRs to automated agent harnesses
Why AI skills files and “make no mistakes” prompts are only requests, and what replaces them in production pipelines
How AI-generated pull requests are overwhelming open-source maintainers (and why projects like cURL are pushing back)
Personalized “software factories”, spec-driven development (GitHub Spec Kit), and cutting AI documentation noise with BLUF (Bottom Line Up Front)
What open-source Rust clones of Adobe mean for the future of SaaS
For infrastructure engineers, DevOps practitioners, and software architects who want to move past one-shot prompting and run coding agents on production infrastructure without losing control.
Timestamps:
00:00:00 - Why “Make No Mistakes” Is a Polite Request
00:00:38 - How Open Source Terraform Changed Provisioning
00:01:59 - Why API-First Services Matter Beyond AWS and Azure
00:04:19 - How AI Agents Changed Terraform Provider Updates
00:07:03 - Why “Make No Mistakes” Won’t Stop Your Agent
00:10:31 - Bringing Determinism Back to Agentic Workflows
00:13:07 - How AI Pull Requests Are Overwhelming Open Source
00:15:41 - Why Every Engineer Is Building a Software Factory
00:18:52 - Why AI-Generated Documentation Slows Down Humans
00:23:12 - Using Agents to Filter Noise and Summarize Specs
00:27:02 - Escape Hatches for Automated Terraform Applies
00:29:03 - Where the Joy in Engineering Lives Now
00:31:24 - Spec-Driven Development and the Return of Architecture
00:34:51 - Open-Source Rust Clones, Reverse Engineering, and SaaS
00:38:19 - How to Build Software Products People Actually Want

Guest: Kerim Satirli, Senior Developer Advocate at HashiCorp, an IBM company.
https://www.linkedin.com/in/ksatirli
#Terraform #CodingAgents #DevOps

Lead Architect Carlos Kelkboom shares his rules for coding agents and how great engineers architect software with AI. One of the great ones is: he throws away generated code the moment he doesn't understand it. He breaks down why coding agents fail at iterative changes, why his main agent never touches files, and how he scaffolds projects for one-shot execution.
In this video, we cover:
Why AI coding agents struggle with iterative changes (SlopCodeBench) and excel at one-shot execution
How to manually scaffold code, types, and folder boundaries before letting sub-agents run
Designing a distributed Markdown and indexed-search graph harness for progressive context disclosure
Using Git worktrees to run parallel coding agents and merge the best implementations
Why dynamic memory and the heap need to disappear in favor of deterministic programming languages
How AI bubbles and solo coding impact junior engineers and team collaboration
This episode is for software engineers, tech leads, and system architects who want to move beyond "vibe coding" and build maintainable, deterministic production software with AI agents.
Connect with Carlos:
https://www.linkedin.com/in/carlos-k-4016b8225

Timestamps:
00:00:00 - Why Carlos Throws Away Working AI Code
00:00:53 - Why Pull Requests and Team Collaboration Are Dying
00:03:50 - The Trap of Starting Projects You Never Finish
00:06:06 - Escaping the Solo AI Developer Bubble
00:11:54 - How to Scaffold Code Before Using AI Agents
00:15:54 - Learning New Programming Languages With AI Agents
00:21:14 - Why We Must Get Rid of the Heap
00:28:32 - Building a Shared Language Across Engineering Teams
00:31:31 - Vibe Coding vs Deep Software Engineering
00:40:12 - Code Is Cheap: Git Worktrees for Parallel Agents
00:41:45 - How Deep to Scaffold Before the Agent Runs
00:45:09 - Democratizing Software, Missing Juniors, and the AI Bubble
00:53:33 - Designing a Graph Harness for AI Agent Context
00:58:18 - Why His Main Agent Never Touches Files
01:03:07 - Throw Away Code You Don't Understand
01:06:25 - Rethinking Code Reviews and Mutation Testing for AI
#CodingAgents #SoftwareEngineering #AgenticEngineering

What happens when coding agents see your entire system: code, services, infrastructure and live traffic, all connected? Most agents are blind to everything outside the repo. Adyen Staff Engineer Mat Jones explains what changed once theirs weren't, and why he calls it somewhat scary.
In this video, we cover:
Running five or six coding agents in parallel with git worktrees and Atrium, an internal tool that lets agents coordinate and share memory
Architect: nine connected graphs in Neo4j covering services, code, DNS, data stores and infrastructure
Ingesting 7 million trace spans a second with Kafka and Apache Flink, and what collapsed on the first try
Sentinel: anti-pattern monitoring, with agents that raise the fix as a merge request
MCP vs APIs vs CLI tools, and how to deal with tool bloat
This episode is for software engineers and platform teams who want coding agents to reason about real systems, not just the repo in front of them. You'll leave with a concrete place to start and an honest view of the risks.

00:00:00 - Intro
00:00:42 - Running Five Coding Agents in Parallel
00:01:35 - Atrium: How Agents Avoid Stepping on Each Other
00:06:57 - Why Graphs Save Coding Agents Tokens
00:12:25 - What Is Architect? Nine Graphs in One Database
00:16:54 - What Broke at 7 Million Spans a Second
00:20:37 - The Timeline From Proof of Concept to Production
00:27:59 - Tracing Code Changes to Critical Payment Flows
00:30:26 - How to Start: Build Scrappy, Then Earn Buy-In
00:35:07 - Making a Codebase Agent Friendly With Skills and Hooks
00:42:07 - Why You Should Start With a Service Graph
00:44:46 - The Downside of Everyone Seeing Everything
00:48:34 - Sentinel: Agents That Find and Fix Anti-Patterns
00:53:01 - Are We Going Too Fast With AI?
01:01:55 - Two Engineers, Four Months: Why Big Bets Got Cheaper
01:07:11 - MCP vs CLI Tools and the Tool Bloat Problem

Guest: Mat Jones, Staff Engineer at Adyen
https://www.linkedin.com/in/mathewwjones
#codingagents #softwareengineering #platformengineering