AI Agent Workflows: Inside Garry Tan’s gstack Setup

Garry Tan, a high-profile founder and investor, recently published an opinionated collection of reusable Claude Code skills he calls “gstack.” The setup frames software development as an orchestrated set of AI agent roles — CEO, engineer, reviewer, designer — each represented by a specialized prompt or skill. The result is a repeatable workflow that many developers and founders are already experimenting with to accelerate feature design, implementation, and review.

What is gstack and why does it matter?

At its core, gstack is a modular collection of AI-driven skills: reusable prompt files that instruct an AI to behave in a particular role. Instead of issuing a single broad prompt like “build this feature,” gstack breaks the work into distinct agentic roles that mimic an engineering organization. That structural framing — simulating teams and handoffs — is the key idea behind modern AI agent workflows.

Why this matters today:

• It promotes specialization: different skills focus on ideation, engineering, code review, security checks, design, and documentation.
• It increases repeatability: opinionated skill files capture best practices so outputs are consistent across runs and projects.
• It scales reasoning: chaining agents lets each agent concentrate on a narrower problem, improving correctness and coverage.

How do agent-based workflows change development?

Agent-based workflows reorganize the cognitive flow of product development. Instead of a single prompt producing many outputs, a pipeline of role-specific agents produces higher‑quality artifacts by dividing responsibilities and enabling iterative checks. This mirrors how engineering organizations structure work: product direction, engineering implementation, code review, and QA — each step becomes a distinct skill in an AI-first pipeline.

Key components of a typical agent workflow

• Ideation agent (acts like a CEO or PM) — evaluates product hypotheses and prioritizes features.
• Engineering agent — writes code or scaffolds implementations from clear task descriptions.
• Automated reviewer agent — inspects for bugs, edge cases, and security concerns.
• Design and docs agent — produces user-facing explanations, README updates, or UX suggestions.

How does gstack actually work in practice?

Gstack exemplifies an explicit, opinionated approach to agent workflows by offering a sequence of skill files for common developer tasks. A typical run might look like this:

1. Run the CEO skill to vet the idea and produce acceptance criteria.
2. Use the engineering skill to convert acceptance criteria into code scaffolding and tests.
3. Invoke the reviewer skill to run static checks, security heuristics, and suggest fixes.
4. Use documentation and design skills to generate user docs, changelogs, and design notes.

By chaining these steps, an engineering team can move from concept to review-ready artifacts faster while preserving a human-in-the-loop for critical decisions. The pattern favors iterative refinement and automated checks over ad-hoc prompt attempts.

Practical example: From idea to review

Imagine a founder has a micro-feature idea. Using an agent workflow, the sequence may be:

• CEO agent summarizes user need and writes acceptance criteria.
• Engineer agent generates feature code, tests, and deployment notes.
• Reviewer agent inspects dependencies, flags potential vulnerabilities, and suggests mitigations.
• Docs agent drafts user-facing instructions and sample usage.

This hands-off automation does not replace engineers; rather, it augments them, allowing human contributors to focus on higher-level judgment, edge-case handling, and integration tasks.

What are the benefits and what are the criticisms?

Proponents emphasize productivity gains and reproducibility. But critics raise concerns about overclaiming, novelty, and the limits of prompt-based systems.

Benefits

• Faster prototyping: structured agent flows reduce back-and-forth and accelerate iteration.
• Consistency: opinionated skills embed style guides and guardrails for uniform outputs.
• Auditability: modular skills can be versioned and reviewed independently.

Common critiques

• Not magical: many developers already employ similar prompt templates directly in their workflows.
• Overhype risk: public claims about “god mode” or perfect bug-finding can set unrealistic expectations.
• Reproducibility vs. novelty: reusing prompts can be valuable, but the gains depend on the underlying model’s capability and context quality.

How secure and reliable are these AI-driven pipelines?

Security and reliability are central concerns when agents generate or modify code. An agent pipeline that includes an automated security reviewer can catch easily identifiable issues, but it cannot replace comprehensive security audits or human expertise. Best practices include:

1. Keep humans in the loop for final approvals and release decisions.
2. Run independent static analysis and dynamic testing tools as part of CI.
3. Version and review skill files to maintain provenance and change history.
4. Limit access and apply least-privilege principles when agents interact with production systems.

For a deeper look at agent security and protections, see our coverage on AI Agent Security: Risks, Protections & Best Practices, which outlines safeguards teams should adopt when deploying agentic systems.

What are the best practices for adopting AI agent workflows?

Teams that adopt agent workflows successfully follow a set of pragmatic rules. Consider this checklist:

• Start small: apply agent workflows to low-risk or experimental features first.
• Define clear acceptance criteria for each agent handoff.
• Version-control skill files and treat them like code — review, test, and iterate.
• Measure outcomes: track time-to-first-PR, bug rates, and developer satisfaction.
• Combine agent outputs with traditional CI/CD and human code review.

For teams building agentic developer tools, the lessons align with our reporting on agentic automations and developer workflows. See Agentic Coding Automations: Streamlining Developer Workflows and How to Build AI Agents: Playful Guide for Developers for practical guides and examples.

How should startups think about ROI from agent workflows?

Startups should evaluate AI agent workflows based on measurable improvements to velocity, quality, and developer bandwidth. Consider the following impact areas:

• Time saved on routine tasks (boilerplate code, documentation, test scaffolding).
• Faster hypothesis testing and feature validation.
• Reduced context-switching for senior engineers who can delegate repeatable tasks to agents.

However, ROI varies by team maturity, tooling integration, and the model’s capabilities. Startups that treat agent workflows as a component of a broader engineering system — with tests, CI, and human oversight — see the most sustainable gains.

What limitations should teams recognize?

Even the most opinionated skillsets have boundaries. Key limitations include:

• Context window constraints and model hallucinations — agents can miss details if not fed complete context.
• False confidence — agents may assert correctness even when outputs are incomplete or insecure.
• Maintenance overhead — skill files require upkeep to stay aligned with evolving standards and dependencies.

Teams must monitor outputs, log agent decisions, and retain human review steps, especially for production-critical code.

How can you get started with agent workflows in your organization?

Here’s a practical starter plan:

1. Identify a pain point (e.g., repetitive doc updates or test scaffolding).
2. Create a minimal set of skills that model the human roles needed for that task.
3. Integrate the pipeline with your repo and CI as a preview step, not an automatic deploy.
4. Collect metrics and feedback from engineers before expanding scope.

By piloting on a small scale, you reduce risk and learn how agentic tooling best augments your existing processes.

Conclusion: Is gstack revolutionary or incremental?

Gstack is instructive not because it is mystical, but because it codifies a clear, opinionated approach to building agent pipelines. It synthesizes several practical ideas into a shareable format: role-based skills, modular workflows, and reproducible engineering practices. Whether gstack is viewed as a breakthrough or a refined pattern depends on how you value packaging, opinionation, and accessibility.

For founders and engineers, the takeaway is straightforward: structuring AI interactions as a set of cooperating, role-specific agents often yields better output than single-shot prompts. Combine that structure with rigorous review processes, and agentic workflows become a pragmatic productivity multiplier rather than a magic bullet.

Further reading

Explore related analysis on agentic tools and developer workflows in these deep dives:

• How to Build AI Agents: Playful Guide for Developers
• Agentic Coding Automations: Streamlining Developer Workflows
• AI Agent Security: Risks, Protections & Best Practices

Ready to experiment with AI agent workflows?

If you’re building products or developer tools, start by creating one reproducible skill that models a human role on your team. Track the impact, iterate, and scale the pipeline as confidence grows. Subscribe to Artificial Intel News for practical guides, templates, and case studies that help teams adopt safe, measurable agentic workflows.

Call to action: Try implementing a three-step agent pipeline this week — ideation, code scaffold, and automated review — and share your lessons with our community to help shape best practices for AI agent workflows.