Something Big Is Happening: A Response to Matt Shumer's Warning About Autonomous Agent Tools

The Warning We Can't Ignore

When Matt Shumer's article "Something Big Is Happening" went viral this week, it struck a nerve across the AI development community. Drawing parallels to February 2020—when early warnings about COVID-19 were mostly ignored—Shumer argues that we're at a similar inflection point with autonomous AI agents. The technology is advancing rapidly, the richest institutions are investing trillions, and yet most of us aren't paying close attention to what's coming.

But Shumer makes another crucial observation that deserves deeper discussion: "Autonomous agent tools are in a sad state." As someone who has been building Sweet! CLI—a terminal-first autonomous company operator—we've arrived at similar conclusions through a different path. The problem isn't that AI isn't powerful enough; it's that our tools and architectures aren't designed for what's coming next.

Why Current Agent Architectures Are Failing

At Sweet! CLI, we've spent the last year analyzing why most autonomous agent tools struggle with real-world software development tasks. The issues Shumer hints at aren't just implementation problems—they're architectural problems:

1. The Orchestration Bottleneck

Most agent frameworks treat AI as the implementer rather than the orchestrator. They attempt to make AI write code directly, manage git operations, handle deployments—all through bespoke APIs and custom implementations. This creates several problems:

• Reinventing the wheel: Why build a custom git client when git already exists and works perfectly?
• Limited tool ecosystem: Custom APIs can't leverage decades of existing developer tooling.
• Combinatorial complexity: Every new capability requires new custom implementations.

2. The Context Management Problem

As Shumer notes, agents struggle with maintaining context across complex tasks. The real issue is that most agent systems try to keep context within the AI model itself rather than in the environment where it naturally belongs.

# Traditional agent approach:
$ agent "build me a web app"
# → Agent tries to remember everything: dependencies, 
#   file structure, progress, errors...

# Sweet! CLI approach:
$ sweet start "deploy to production"
# → AI orchestrates existing tools (git, docker, npm, etc.)
# → Context lives in the actual project files, git history,
#   and terminal session—where it belongs

3. The Integration Gap

Autonomous agents that can't seamlessly integrate with existing development workflows are destined for the "toy project" graveyard. Developers don't want to learn new interfaces; they want their existing tools to become more intelligent.

What If We're Asking the Wrong Question?

Shumer's article focuses on whether autonomous agents will replace developers. But what if that's the wrong question entirely? At Sweet! CLI, we believe the more interesting question is:

When we stop asking "how do we make AI code faster?" and start asking "what becomes possible when implementation is mostly autonomous?", we discover different architectural priorities:

The Terminal: The Missing Piece

What most agent frameworks miss is that developers already have a perfect interface for autonomous operation: the terminal. Every tool developers use already has a CLI. Every deployment pipeline, every test runner, every build system—they all speak the language of stdin/stdout.

By building AI that can operate existing CLI tools rather than replacing them, we solve Shumer's "sad state" problem:

• No reinvention: Use git, docker, npm, terraform as-is
• Infinite extensibility: Any CLI tool becomes an AI capability
• Natural context: Terminal output provides real-time feedback
• Proven reliability: Decades of tooling maturity

What "Something Big" Actually Looks Like

If Shumer is right (and we believe he is), then the "something big" isn't just better code generation. It's a fundamental shift in how software companies operate:

The Path Forward

Shumer is correct that we need to pay attention. The signs are there:

• AI models are improving predictably (not exponentially, but steadily)
• Trillions in investment are flowing into AI infrastructure
• Early adopters are seeing transformative productivity gains
• The technology actually works for real tasks

But to move from "sad state" to transformative tooling, we need to embrace different architectural principles:

1. Orchestration over implementation: AI should use tools, not replace them
2. Terminal-first design: Work where developers already work
3. Context in environment: Let files, git, and logs carry state
4. Incremental autonomy: Start by enhancing existing workflows

Conclusion: Don't Just Watch—Build

The most important lesson from Shumer's February 2020 analogy isn't about passive observation. In early 2020, the people who recognized what was coming didn't just watch—they prepared, adapted, and built new solutions.

If autonomous agent tools are indeed in a "sad state," that's not a reason for pessimism. It's an opportunity for those of us building the next generation of tools. The terminal-first, orchestration-based approach we've taken with Sweet! CLI is one path forward, but there will be many others.