Nov 11, 16:00
Advanced Claude Code Orchestration: A Decision Framework for Skills, Subagents, Goals, and Workflows
November 16 - 19, 2026
New York, US & Online
AI Coding Summit NYC
See how AI transforms software engineering
Bringing AI coding best practices to NYC. Two days of practical talks and workshops on coding agents, AI code review, context engineering, prompt-driven development, and shipping production software with AI at scale. Connect with the East Coast AI developer community and learn proven workflows from engineering leaders who use AI tools daily.
Upcoming workshops
Nov 18, 14:00
AI-Native Product Engineering
WorkshopPro
AI is writing more code every day. The skill that will not get automated is knowing what to build. And how to ship it with confidence when agents do most of the implementation.
In this full-day workshop, you will practice product engineering judgment upstream of the code, then put an AI-native delivery loop underneath it. You will learn how to validate a problem before you commit to a solution, understand the users you are actually building for, prioritize ruthlessly, and make reversible product decisions. Then you will set up the systems that let agents implement safely: shared intent and context, durable primitives, verification gates, feature flags, and outcome review instead of reading every diff.
What you will learn:
1. Validate before you build: surface real user pain, not solution-shaped stories
2. Align intent with agents: shared understanding, options with trade-offs, and clear "done"
3. Prioritize what deserves to exist (cut ruthlessly)
4. Design two-way doors: use flags and reversible decisions so you can ship and learn
5. Build agent-ready primitives: structure the repo so agents reuse building blocks instead of inventing slop
6. Close the delivery loop: tests, AI review, CI, preview environments, and risk-aware shipping without babysitting every line
7. Keep the system improving: friction logs, cleanup, and feedback loops after you ship
Who it's for: Software engineers with professional experience who already use (or are starting to use) coding agents. No specific stack required. PMs and tech leads welcome.
Format: One full day, hands-on. Bring a laptop, take notes, and have your preferred agent ready to go!
In this full-day workshop, you will practice product engineering judgment upstream of the code, then put an AI-native delivery loop underneath it. You will learn how to validate a problem before you commit to a solution, understand the users you are actually building for, prioritize ruthlessly, and make reversible product decisions. Then you will set up the systems that let agents implement safely: shared intent and context, durable primitives, verification gates, feature flags, and outcome review instead of reading every diff.
What you will learn:
1. Validate before you build: surface real user pain, not solution-shaped stories
2. Align intent with agents: shared understanding, options with trade-offs, and clear "done"
3. Prioritize what deserves to exist (cut ruthlessly)
4. Design two-way doors: use flags and reversible decisions so you can ship and learn
5. Build agent-ready primitives: structure the repo so agents reuse building blocks instead of inventing slop
6. Close the delivery loop: tests, AI review, CI, preview environments, and risk-aware shipping without babysitting every line
7. Keep the system improving: friction logs, cleanup, and feedback loops after you ship
Who it's for: Software engineers with professional experience who already use (or are starting to use) coding agents. No specific stack required. PMs and tech leads welcome.
Format: One full day, hands-on. Bring a laptop, take notes, and have your preferred agent ready to go!
Nov 23, 15:00
Build a Production Agent Harness
WorkshopPro
Most agents fail in production for reasons the model can't fix. In this workshop, we start from an agent that fails and lies about it, then harness it step by step until it does the job reliably, without a single change to the prompt. Workshop outline:
1. The failing agent: why it breaks, and why it claims success anyway
2. The agent loop, built up from an empty loop
3. A tool registry and context management
4. Guardrails, context compression and retries that hold whichever model sits underneath
5. Deterministic verification, so the agent fails honestly instead of claiming success
6. Moving secrets and fragile steps, like login, out of the model and into the harness
7. A harness plan for the agents you're building
1. The failing agent: why it breaks, and why it claims success anyway
2. The agent loop, built up from an empty loop
3. A tool registry and context management
4. Guardrails, context compression and retries that hold whichever model sits underneath
5. Deterministic verification, so the agent fails honestly instead of claiming success
6. Moving secrets and fragile steps, like login, out of the model and into the harness
7. A harness plan for the agents you're building
Nov 24, 15:00
Orchestrating Agent Swarms: Automating Migrations with Agent Loops in Antigravity
Workshop
The Problem
Enterprise engineering teams are burdened by aging Node.js/Express monoliths tightly coupled to fragile templates and untyped data layers. Manually refactoring these massive codebases into modern cloud-native architectures is notoriously slow, introduces severe regressions, and creates massive code review bottlenecks for developer workflows.
The Solution
Instead of manual rewrites, modern teams can harness advanced agentic-programming patterns to orchestrate dedicated agent swarms. This workshop guides advanced engineers through an automated refactoring and migration pipeline using Google Antigravity. Attendees will learn how to leverage structured task planning, parallel subagents, and automated LLM code reviews to transform a legacy Express monolith into a strongly-typed Next.js application.
Key Takeaways
By the end of this hands-on session, attendees will be able to:
1. Orchestrate Agent Swarms: Author deterministic task plans and custom Agent Skills that direct parallel subagents to audit legacy middleware, API contracts, and schemas.
2. Execute AI Refactoring & Migrations: Automate large-scale structural code generation while establishing rigorous LLM-assisted code review gates.
3. Implement AI-Assisted Testing & QA: Set up autonomous CI/CD verification loops where agents execute failing tests, parse error logs, and self-correct code until runtime parity is perfectly proven.
Enterprise engineering teams are burdened by aging Node.js/Express monoliths tightly coupled to fragile templates and untyped data layers. Manually refactoring these massive codebases into modern cloud-native architectures is notoriously slow, introduces severe regressions, and creates massive code review bottlenecks for developer workflows.
The Solution
Instead of manual rewrites, modern teams can harness advanced agentic-programming patterns to orchestrate dedicated agent swarms. This workshop guides advanced engineers through an automated refactoring and migration pipeline using Google Antigravity. Attendees will learn how to leverage structured task planning, parallel subagents, and automated LLM code reviews to transform a legacy Express monolith into a strongly-typed Next.js application.
Key Takeaways
By the end of this hands-on session, attendees will be able to:
1. Orchestrate Agent Swarms: Author deterministic task plans and custom Agent Skills that direct parallel subagents to audit legacy middleware, API contracts, and schemas.
2. Execute AI Refactoring & Migrations: Automate large-scale structural code generation while establishing rigorous LLM-assisted code review gates.
3. Implement AI-Assisted Testing & QA: Set up autonomous CI/CD verification loops where agents execute failing tests, parse error logs, and self-correct code until runtime parity is perfectly proven.
Upcoming
From First Prompt to Reviewed Change: OpenCode with an Open-Weight Model
Workshop
Learn how to use OpenCode with an open-weight model to make a small, practical change in a sample application—from defining the task through reviewing and testing the result before you accept it.Together, we’ll choose the most suitable model provider—GLM, Kimi, or DeepSeek—based on reliable access and the group’s setup. You’ll see how to work with an AI coding agent in a controlled, repeatable way: set clear acceptance criteria, keep the scope manageable, inspect every proposed change, and validate it with tests.What we’ll coverSet up the challenge: Review the sample repository, define a focused task, and agree on clear acceptance criteria.Configure your tools: Connect OpenCode to the selected model provider and understand credentials, access requirements, and potential usage costs.Build with an agent: Use OpenCode to plan and implement a small, well-bounded feature.Review before accepting: Inspect the generated diff, run the test suite, and learn how to diagnose or recover from an unsuccessful attempt.Make the process reusable: Turn the workflow into a practical checklist you can apply to future coding tasks.Ask questions: Bring your setup, workflow, and agent-development questions to the final Q&A.By the end of the session, you’ll have:A reviewed and tested change in a sample appHands-on experience guiding an AI coding agent through a scoped taskA reusable checklist for planning, reviewing, and validating agent-generated code
Past workshops
Recording pending
Claude Code: Black Belt
Workshop
Stop prompting. Start orchestrating. In four intense hours you'll go from using Claude Code like a faster autocomplete to commanding it like a senior engineer commands a team: engineering its context, deploying fleets of subagents, locking it down with hooks, and turning it loose on work that runs without you.Every Claude Code user hits a ceiling where the easy wins run out. The agent handles small stuff beautifully, then loses the thread on anything real. The difference between that ceiling and real mastery isn't better prompts. It's control. This workshop is about control.You'll spend the whole four hours inside CLASH, a real full-stack application, handed to you fully built so nothing stands between you and the hard parts. A serious codebase is the point: it's the only place agentic engineering shows you whether it actually holds up.The throughline is context. Treated carelessly, the context window fills with noise until the agent drifts. Treated as a resource you engineer, it becomes the biggest lever you have. From there the toolkit opens up. Repeatable work becomes a reusable Skill. Noisy, exploratory work goes to subagents that run in their own isolated context, several at once when the job allows. Hard rules become hooks the agent cannot cross. Your own systems come into reach through MCP.Then you let go of the wheel, carefully. The same agent that pairs with you can run headless in a pipeline, drive a long task to a defined finish on its own, or live inside your software through the Agent SDK. We close by setting two greenfield methodologies, Spec Kit and BMAD, side by side, so you leave knowing not just how to drive the agent but which approach fits which problem.Two ideas hold it together: context is king, and you push it, you own it. This was never about generating code faster. It's about staying in command while the agent does more.This is an advanced session for engineers, tech leads, and architects who already use Claude Code every day and want to reach the top of the curve. We move fast, and we start in the deep end.