AI-Native Software Engineering: Ship More, Move Faster, Build with Confidence.
Luis Vieira
Staff Software Engineer
Your team adopted AI. Why hasn’t delivery improved?
AI coding tools make implementation faster. But typing code is only one part of getting software safely from idea to production.
Teams still need to turn ambiguous requests into clear plans, give agents the right context, understand how changes affect existing systems, verify that the work is safe, and decide what should reach production.
As agents become more capable, the bottleneck shifts. When code is cheap to produce, context, judgment, verification, and coordination become the limiting factors.
This requires new agentic engineering practices: structuring work for delegation, giving agents clear constraints, running independent workstreams in parallel, and knowing where human judgment matters most.
This workshop gives you a practical method for improving software delivery with agents. You’ll learn to identify SDLC bottlenecks, use multiple cloud agents in parallel, and build the context, guardrails, proof of work, feedback loops, and evals needed to improve them over time.
What you’ll learn
Learn how to find delivery constraints, design trustworthy agent workflows, and improve them through evidence, feedback, and evals.
Understand why faster code generation does not automatically lead to faster software delivery.
Identify how constraints shift from implementation to context, coordination, review, verification, and risk management.
Understand the need to optimize for flow across the whole delivery system, not just coding speed.
Decide what humans should own and what agents can execute autonomously.
Create bounded tasks that agents can execute with clear outcomes and constraints.
Manage fleets of parallel cloud agents
Identify common constraints across planning, implementation, review, testing, release, and operations.
Identify where an agent can safely reduce waiting, rework, or uncertainty.
Define bounded workflows for use cases such as triage work, code review, and task validation
Give agents the right context through rules, skills, and MCP-enabled tools.
Turn engineering standards into executable constraints Use strict types, lint rules, tests and CI checks to prevent undesirable patterns
Require proof of work: evidence, checks performed, confidence, and next actions.
Use traces and failed runs to understand where a workflow breaks down.
Build a small eval set to measure whether changes make the workflow more reliable.
Workshop agenda
The AI Coding Plateau
Understand why faster code generation does not automatically lead to faster delivery. Learn how bottlenecks shift across the SDLC and how to optimize for flow, not just coding speed.
The AI-Native engineer
Learn how to divide work between humans and agents, structure tasks for delegation, coordinate parallel cloud agents, and optimize for human attention while maintaining quality.
Agents Across the SDLC
Explore common bottlenecks from planning to production. Learn where agents can safely help, where human judgment remains essential and prioritize the workflows where autonomy creates the most value.
Implement a Trustworthy Agent Harness
Build the environment agents need to work reliably with: context, tools, permissions, executable constraints, proof of work and verification
Improving the Harness
Use traces, failed runs, and evals to understand where workflows break down. Improve rules, context, skills, tools, and subagents, and increase autonomy based on evidence.
Who this workshop is for
Software engineers using AI coding tools looking to improve their team SDLC
Tech leads and engineering managers who want to understand how to increase engineering output using AI
Engineering leaders who adopted AI tools but aren't seeing it in delivery speed or business outcomes and want to understand why and fix it
What's included
Live sessions
Learn directly from Luis Vieira in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
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Frequently asked questions
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