Yes, every major tech company is racing to build agent orchestration tools. Yes, Gartner expects agentic orchestration to redirect $550B in global software and services spend by 2029.  But the majority of engineering teams have no business adopting agent orchestration right now.

I’m not saying orchestration isn’t real or important. It is. It’s probably the most significant shift in how we’ll build software since the cloud. But the gap between Steve Yegge running 30 Claude instances in Gas Town and where most teams actually are –  struggling to get consistent value from a single Copilot – is enormous.

This article is my attempt to cut through the noise. I’ll tell you what’s actually working, what’s still vaporware, who should be paying attention, and what the real path forward looks like for teams that want to be ready when orchestration goes mainstream.

Keep Reading If…

You’re running an engineering org of 10+ people, and you’ve already gotten real value from AI coding tools. Not “we have Copilot licenses”, I mean your team has changed how they work because of AI assistance.

You’ve hit the ceiling of single-agent workflows. Context gets lost. You can’t parallelize. You’re spending as much time babysitting the AI as you would just writing the code yourself.

You are at what Steve Yegge calls “Stage 5-7” of the evolution: you’re comfortable with CLI agents, you’ve turned off permission prompts, you regularly run 3-5 instances, and you’re starting to feel the limits of hand management.

Stop Here If…

You haven’t mastered single-agent workflows yet. This is the biggest mistake I see. Teams jump to orchestration because single-agent isn’t working, but orchestration won’t fix that; it will amplify the dysfunction. If you can’t get consistent value from one agent, you’ll get consistently amplified chaos from ten.

Your team is under 10 engineers. The coordination overhead will eat you alive. You don’t have enough parallelizable work to justify the complexity. Stick with conductor-mode tools like Cursor or Claude Code until you scale.

You’re looking for cost savings. Multi-agent orchestration is currently expensive. Some Gas Town users report $200+/month just for Claude API costs. You’re trading money for throughput, not saving money.

You don’t have solid CI/CD and code review practices. Orchestrators don’t replace engineering discipline – they amplify whatever you already have. Bad processes at 10x speed is just 10x the technical debt.

From Conductors to Orchestrators

Forget the marketing speak about “AI ecosystems” and “autonomous agents.” Here’s what’s actually happening: we’re moving from programming to management.

In the conductor model (where most teams are today), you work with one AI agent interactively. You prompt, it responds, you review, you iterate. You’re still in the loop for every decision. Tools like Cursor, Claude Code, and Gemini CLI are conductor tools – fancy pair programmers.

In the orchestrator model (where the frontier is heading), you manage a fleet of agents working in parallel. You define goals, decompose tasks, and review outputs. You’re not writing code – you’re directing traffic.

Here’s my strong opinion: most developers will hate this transition. We became engineers because we like building things with our hands. Managing a fleet of AI workers feels like being promoted to a job you didn’t apply for. But the engineers who figure out how to be effective orchestrators will have a massive advantage over those who cling to hands-on coding.

What’s Working Right Now

The Parallel Execution Tools

These are tools that let you run multiple coding agents simultaneously. They’re the simplest form of orchestration and the most proven.

Claude Squad spawns multiple Claude Code instances in tmux panes. Dead simple. If you’re at Stage 5-6 and want to dip your toes in, start here.

Conductor Build by Melty Labs gives each agent its own isolated Git worktree. The dashboard showing “who’s working on what” is genuinely useful. This is orchestration for people who aren’t ready for full orchestration.

Code Conductor is GitHub-native – tasks are GitHub Issues, agents claim them, work in branches, and open PRs automatically. If your workflow is already GitHub-centric, this is the natural extension.

The Full-Scale Orchestrator: Gas Town

Steve Yegge’s Gas Town is the most ambitious attempt at coding orchestration I’ve seen. A Go-based system for coordinating 20-30+ concurrent agents with seven distinct roles (Mayor, Crew, Refinery, Witness, Polecats, Deacon, Dogs) and its own work-tracking system called “Beads.”

Gas Town is either a glimpse of the future or an elaborate meme. Possibly both. Early users report going from 5 PRs in 3 hours to 36 PRs in 4 hours. One user described it as “all the friction has been taken out of programming.” Another said it “smells like the early days of blogging about blockchains.”

Yegge is explicit that you need to be at Stage 6-7 before Gas Town makes sense. The system is intentionally complex – he compares it to Kubernetes and Temporal. If you’re not already comfortable running 5-10 agents manually, Gas Town will overwhelm you. 

The Spec-Driven Platforms: Where I’m Most Bullish

This is where I think the real breakthrough is happening. Parallel execution is powerful, but it still relies on ad-hoc prompting. Spec-driven development changes the game by making specifications, not prompts, the source of truth. 

Unlike prompts, specs live in repositories, allowing teams to version control changes over time. Specs contain executable logic and are “living” documents that can be fed to AI to generate code. And, unlike prompts, specifications allow multiple AI agents to work in parallel on a single project. 

GitHub Spec Kit structures development into specify > plan > tasks > implement phases. The insight is that specifications become executable – they’re not documentation; they’re the actual input that drives code generation.

Runbooks is what I’d call “multiplayer spec-driven development.” It turns AI-assisted coding from a single-player activity into a team sport with shared spec libraries, collaborative workflows, and audit trails. This matters because enterprise adoption requires collaboration and governance – you can’t scale individual vibe coding across a 50-person engineering org.

Zenflow by Zencoder implements a Plan > Implement > Test > Review workflow with multi-agent verification – different models critique each other’s work. Their research shows ~20% improvement in code correctness. The “committee approach” of having Claude review OpenAI’s output (and vice versa) is clever and genuinely useful.

My strong opinion: spec-driven development will win. Ad-hoc prompting doesn’t scale. It’s not auditable, not reproducible, and not teachable. Specs are all three. Sean Grove from OpenAI nailed it: “The person who communicates the best will be the most valuable programmer. The new scarce skill is writing specifications that fully capture your intent.”

What’s Not Working 

The 10x Productivity Claims Are Mostly BS

Every vendor claims 10x productivity gains. Stanford research consistently shows improvements closer to 20%. Zencoder’s own research shows ~20% improvement in code correctness.

The honest framing: Orchestration lets you parallelize work and reduce context-switching. If you have 10 parallelizable tasks, you can do them simultaneously instead of sequentially. That’s not 10x productivity – it’s 10x throughput on parallelizable work, which is a much narrower claim.

Debugging Multi-Agent Systems is a Nightmare

One practitioner described it as “whack-a-mole: fix one issue with some prompt engineering and then create three more.” Error logs are cryptic. There’s no clear troubleshooting guide. When agents conflict or get stuck in loops, figuring out what went wrong requires detective work that can eat up all the time you supposedly saved.

My take: this is the single biggest obstacle to mainstream adoption. Until observability and debugging tools catch up, orchestration will remain a tool for experts who can debug by intuition.

The Cost Story is Ugly

Running agent fleets making thousands of LLM calls daily gets expensive fast. Some organizations are spending more on AI API costs than on developer salaries. The “Plan-and-Execute” pattern (frontier models for orchestration, cheap models for execution) can reduce costs by 90%, but most teams haven’t figured this out yet.

The uncomfortable math: if you’re paying $200/month in API costs to save 2 hours of developer time per week, you need to be paying developers less than $25/hour for that to make economic sense. For most teams, the ROI only works if orchestration enables things that were previously impossible, not just faster.

Trust Calibration is Unsolved

Teams either over-trust agents (letting them run unsupervised, merging PRs without review) or under-trust them (reviewing every line, defeating the purpose of automation). Neither extreme works. The right calibration depends on task type, agent capability, and risk tolerance – and there’s no framework for figuring this out.

What This Looks Like in Practice for Teams

If Your Team is at Stage 1-4: Don’t Chase Orchestration

Focus on getting real value from single-agent tools. Learn to write effective prompts. Understand what tasks AI handles well vs. poorly. Build intuition for when to trust agent output. This foundation is non-negotiable.

Recommended path: Start with Cursor or Claude Code in interactive mode. Graduate to CLI-based workflows. Turn off permission prompts when you’re confident. Run 2-3 instances manually. Stay here until it feels limiting.

If Your Team is at Stage 5-6: Experiment Carefully

You’re ready to explore orchestration, but don’t go all-in. Start with Claude Squad or Conductor on well-scoped, parallelizable tasks – dependency updates, test coverage expansion, documentation generation. Measure everything. Build observability from day one.

Recommended path: Pick one orchestration tool and one category of tasks. Run for 2-4 weeks. Measure time saved vs. time debugging. If positive ROI, expand scope. If not, identify what broke and fix it before scaling.

If Your Team is at Stage 7+: Go Deep on Spec-Driven

You’ve already proven you can manage multiple agents in a team setting. The next unlock is making that work reproducible and collaborative. Spec-driven platforms like Spec Kit and Runbooks will let you codify your workflows, share them across the team, and maintain audit trails.

Recommended path: Adopt a spec-driven platform. Document your most common workflows as reusable specs. Train your team on spec writing. Build a library of battle-tested specs for your codebase. This is how you scale beyond individual productivity.

For Everyone: Invest in the Fundamentals

Adopt MCP now. Even if you’re not ready for orchestration, MCP-compatible tools will age better than proprietary alternatives.

Build context engineering capabilities. The quality of context you provide agents matters more than which orchestrator you choose.

Establish governance early. When (not if) you scale AI-generated code, you’ll need audit trails, compliance checks, and security policies. It’s easier to build these from the start.

Prepare your team for the mindset shift. The engineers who thrive in the orchestration era will be those who embrace management and systems thinking. Start that conversation now.

If you take one thing from this article, let it be this: don’t let FOMO push you into orchestration before you’re ready. The cost of adopting too early (wasted time, frustrated teams, technical debt) is higher than the cost of adopting too late. 

Build the foundations that will make orchestration work when you’re ready. That means mastering single-agent workflows, adopting MCP, investing in spec-driven practices, and preparing your team for the mindset shift from coding to orchestrating.