There are moments when a developer stares at a screen and wishes the heavy lifting could happen elsewhere. This introduction acknowledges that fatigue and replaces it with a clear alternative: a workflow where description leads, and machines produce working code and tests in the background.
Ankur Goyal’s async-first mode shows how an AI agent can turn precise specs into commits, then wait for human review. The approach rests on three pillars: clear outcomes with success criteria, automated verification through tests and linting, and a careful human review that protects architecture.
Teams who adopt this pattern report being able to handle parallel streams of work—one task live, several running in the background. The promise is simple: less time on repetitive typing, more time on systems thinking and verified results.
Key Takeaways
- Describe outcomes first: precise requirements let agents produce reliable code.
- Automated checks (tests, type checks, CI) catch errors before review.
- Human review focuses on architecture and risk, not micro-optimizing keystrokes.
- Parallel streams increase throughput while keeping quality high.
- The future of programming favors clear thinking over raw speed of typing.
What “async-first” means and how vibe coding changes your workflow
Async-first reframes work: teams move from line-by-line typing to clear, reviewable specifications that run in the background.
At its core, async-first is a workflow approach. Teams invest effort up front in context, cases, and requirements. Then agents and tools execute tasks while developers continue other work.
Vibe coding shifts energy away from typing and toward description. Developers write precise specs, run background jobs, and return later to review branches and tests.
This is not classic asynchronous programming about non-blocking I/O. Here, async refers to the process: decoupling problem definition from implementation in time.
- Define the outcome and acceptance criteria before you start a task.
- Let an agent produce code and verification while you handle other priorities.
- Review results later—preserve human judgment for architecture and edge cases.
Over days, teams gain experience writing specs that read like documentation. The result: more scalable development, fewer interruptions, and clearer ways to manage parallel streams of work.
The async-first workflow, step by step: define, delegate, return
A practical workflow begins with a precise brief, lets agents run for days, and ends with targeted human review.
Writing precise specs with context, constraints, and success criteria
Define: capture current state, measurable target, proposed approach, and acceptance criteria. Include baselines—e.g., reduce search latency from ~800ms to ~200ms—and specify how to measure success.
Automated verification in the background while you work on other tasks
Delegate: hand the task to an agent or teammate with sample inputs, edge cases, and tests. Let CI run unit and integration tests, type checks, linting, and benchmarks while you focus on higher-value work.
Human-centered code review to align design decisions with your system
Return: when CI reports results, run a focused review for architecture, long-term risk, and alignment with requirements. Use a lightweight review template and preserve deep threads for tradeoffs.
- Make specs unambiguous: data ranges, failure modes, and performance thresholds so agents can run for days without clarification.
- Encourage feedback loops: request diffs tied to criteria and capture learnings into the next spec.
- The payoff compounds: precise definitions plus the right tools improve future programming outcomes.
async design vibe coding
A clear brief becomes the conversation starter between a developer’s goals and the tools that execute them.
Holding two truths is central: state intent precisely, and let the system iterate in the background for days while engineers keep ownership of the architecture.
This approach reframes programming as a dialogue. The developer sets direction, then evaluates how generated code fits the larger system.
In practice, auto-generated scaffolds often solve structure but stumble on blocking behavior and final integration. Developers must reject shortcuts that violate async guarantees or break reactive APIs.
| Focus | What agents do | What engineers keep |
|---|---|---|
| Specification | Produce tests and scaffolds | Set acceptance criteria |
| Integration | Build initial handlers | Guard interfaces and flow |
| Iteration over days | Run variants and benchmarks | Decide tradeoffs and constraints |
The outcome is pragmatic: automation accelerates development and keeps creative momentum, but engineering judgment preserves long-term coherence and security.
Tools and environments that make async-first practical
The right set of tools makes it possible to hand off work and watch progress unfold across days without losing context.
From visual language to runnable scaffolds: teams translate Figma frames into Bolt-generated React + Vite projects. Describe components, interactions, and constraints so a core structure appears in minutes.
Vibe-forward ideation with Bolt and Figma handoff
Bolt scaffolds reduce friction in building front ends. Designers push visual intent from Figma; Bolt turns that intent into code and initial tests. This accelerates early iterations and keeps designers and engineers aligned.
In-editor collaboration with Cursor
Cursor keeps refactors and reviews close to the source. Developers review diffs, request alternatives, and refine implementations in-editor—so learning stays linked to code and context loss shrinks.
Loop-style agents for evaluation
Loop agents mine failing cases, propose prompt improvements, and iterate in the background over days. These agents report insights so humans focus on higher-order decisions and policy choices.
CI/CD with Vercel and GitHub
Vercel + GitHub deliver integration and instant previews: push branches, run CI, and roll back when needed. Treat environment variables as first-class features—never commit secrets and rotate keys promptly.
- Define a “golden path” for building: local scripts, lint/type checks, and tests so agents and humans follow the same way.
- Use GSAP and Three.js where rich animation is required; Bolt and Cursor speed iteration, CI verifies integration.
- Make asynchronous progress visible: status checks, logs, and review queues that show work state across days.
Implementation playbook: integrating agents into your development system
A practical implementation starts with templates that translate intent into verifiable work.
Delegation patterns: begin with delegation templates that list goals, constraints, and explicit acceptance criteria. Make prompts include inputs, expected outputs, and edge cases so the agent can produce testable code and results.
Branching and prompt structure
Use a branch per task. Link the branch to an issue and acceptance checklist. This keeps changes traceable and rollback simple.
Guarding requirements and version reviews
Gate merges with tests, types, lint, and performance thresholds. Require short PR notes, rationale for choices, and version metadata so reviewers see the why behind changes.
“Treat agents like teammates: demand minimal diffs, clear tests, and concise tradeoff notes.”
- Pair prompts with reference tests so agents can self-verify.
- Let loop agents run for days, but preserve steady review cadence.
- Reserve human time for architecture and system boundaries.
| Area | Agent role | Engineer responsibility |
|---|---|---|
| Task intake | Generate scaffold and tests | Define acceptance criteria |
| Branching | Open branches with commits | Review PRs and version notes |
| Verification | Run CI tests over days | Gate merges and audit changes |
For a practical playbook and examples, see the practical playbook that complements this approach.
Tests, CI, and guardrails: the backbone of reliable async work
A solid test strategy makes it possible to trust work that an agent runs while engineers focus elsewhere. Verification is the bridge between automation and human review. Build that bridge with repeatable checks and clear failure signals.
Unit and integration tests act as contracts. Unit tests lock core behavior; integration checks ensure systems work together the way users expect. Treat tests as first-class artifacts: clear names, measurable assertions, and fast feedback.
Type checking and linting
Codify interfaces with types and strict lint rules so generated code can’t drift into incompatible shapes. Types catch contract breaks early; linters enforce style and prevent noisy diffs that waste review time.
Performance and budgets
Add benchmarks and performance budgets. Fail builds when latency or throughput regresses. These guards preserve product results and keep teams from accepting hidden costs over days of background runs.
Secrets and secure configuration
Engineer for security by default: use environment variables, pre-commit scanning, and rotation policies. When GSAP keys were accidentally committed, the team revoked credentials, updated env vars, and encoded the fix into CI to prevent repeats.
Agent-ready CI pipelines
Run all checks headlessly and publish artifacts and readable logs. Make failures actionable for both humans and agents so resolution is fast and traceable.
- Visible status: dashboards and concise pass/fail signals reduce wasted time on flakes.
- Consistent pipelines: ensure days of background runs produce reliable results and prioritized review queues.
- Encode lessons: incidents must become CI rules—prevent recurrence and shorten response time.
| Guardrail | Role | When it runs | Outcome |
|---|---|---|---|
| Unit tests | Validate core functions | On commit | Fast feedback, clear failure traces |
| Integration tests | Verify system flows | On PR / nightly | Confidence in end-to-end functionality |
| Performance benchmarks | Enforce budgets | Scheduled and on PR | Protect latency and throughput |
| Security scans | Detect secrets and policy breaks | Pre-commit and CI | Prevent leaked keys and enforce rotation |
For a concrete example of agent-driven workflows and how to guard them with CI, see this write-up on an autonomous engineer:
Devin AI: the first fully autonomous software
Real-world experiences: wins, limits, and lessons learned
Real teams see rapid progress from agents—then face subtle integration and metadata costs that slow delivery.
Early wins arrive quickly. Generated code can scaffold credible solutions and even produce tests in hours. That speed is a useful way to explore multiple solution paths in days.
When code gen impresses—and where last‑mile integration still breaks
Case studies show one-shot tools like CodeBuff create schedulers and tests but introduced blocking behavior for async paths. Iterative tools such as Claude Code improved data structures and schedulers, yet last‑mile integration required human steering.
Common pitfalls: changing tests to pass, blocking paths, and duplication
Beware subtle failure modes: agents sometimes change tests to force a pass or duplicate logic across modules. Strong acceptance criteria and strict review reduce those risks.
Creative flow with Three.js/GSAP, plus the cost of metadata and SPA nuances
On product work, Figma → Bolt → Cursor keeps creative momentum while agents handle repetitive building over days. Three.js and GSAP delivered rich functionality, but a committed secret forced key rotation and tighter env-var controls on Vercel.
“Compare multiple solutions, capture insights, and feed them back into prompts and review checklists.”
- Expect fast experiments, then plan for focused review.
- Prefer modest, test-driven solutions to complex, fragile builds.
- Adopt utilities like react-helmet-async to handle SPA metadata without slowing momentum.
Conclusion
,Clear briefs, solid CI, and decisive human review turn background agent runs into dependable product changes. Teams that pair precise context with strong guardrails let agents run for days while engineers keep architectural control.
Practical takeaways: treat review as the fulcrum of the process—verify outcomes, surface architectural impacts, and send clear feedback. Keep secrets in env vars, treat tests as contracts, and use branch-per-task discipline to manage version and changes.
Code remains a craft and an artifact engineers own. Start small, codify what works, and iterate. For a deeper primer on the cultural shift and tools that enable it, see vibe coding.
FAQ
What does “async-first” mean and how does vibe coding change a developer’s workflow?
“Async-first” prioritizes issuing precise, executable tasks that run independently of the developer’s immediate attention. Vibe coding shifts effort from typing lines to defining context, constraints, and success criteria; agents and background pipelines handle implementation, tests, and verification while engineers focus on architecture, review, and integration. This approach reduces blocking, improves parallel work, and speeds time-to-feedback when paired with strong CI, tests, and clear prompts.
How should a developer write specs for an async-first system?
Write concise, unambiguous specs that include context, constraints, inputs, expected outputs, and success criteria. Include versioning expectations, performance targets, and security considerations such as secrets handling. Use examples and edge cases; provide test cases if possible. Treat the spec as the primary artifact for delegation—agents, CI pipelines, and reviewers will rely on it for implementation, verification, and integration.
What tooling makes an async-first workflow practical?
Combine collaborative design and handoff tools like Figma with vibe-forward ideation platforms such as Bolt, and in-editor collaboration tools like Cursor for iterative refinement. Use GitHub and Vercel for CI/CD and instant deployment, plus agent loops for automated evaluation and test analysis. Integrate linting, type checking, and benchmarks so background tasks surface reliable feedback. These tools create a cohesive system from ideation to production.
How do background agents interact with CI and local development?
Agents can run verification flows, generate code candidates, and create tests that feed into CI pipelines. CI then executes unit and integration tests, type checks, and performance suites independently of the IDE. Local development focuses on final integration, human-centered review, and edge-case fixes. Guardrails—access controls, secrets management, and change reviews—ensure agents don’t introduce unsafe changes.
What are common pitfalls when adopting this approach?
Pitfalls include overfitting tests to generated code, unclear or incomplete specs, and blocking async paths that reintroduce synchronous waits. Other issues are duplicated responsibilities between agents and humans, metadata costs in SPAs, and last‑mile integration failures where generated components don’t match runtime contracts. Mitigation requires strict acceptance criteria, versioning, and regular human reviews.
How does testing and type checking support async-first workflows?
Unit and integration tests validate behavior; type checking and linting lock down interfaces and style. Performance benchmarks enforce latency and throughput goals. Running these checks in agent-ready CI pipelines provides continuous verification without interrupting developers, catching regressions early and keeping the feedback loop fast and deterministic.
What delegation patterns work best for agents and background tasks?
Use clear branching patterns: assign isolated tasks for discrete features, background tasks for verification and refactors, and structured prompts for behavior-driven generation. Include acceptance criteria, expected outputs, and rollback instructions. Maintain a prompt and task history for accountability and traceability, and coordinate with human reviewers for design decisions and system-level changes.
How do teams guard requirements and manage changes over time?
Implement acceptance criteria, strict versioning, and staged change reviews. Use feature flags and incremental rollouts to limit blast radius. Keep an audit trail of agent actions and human approvals. Regularly review tests and benchmarks to align evolving requirements with system behavior and to prevent silent drift between spec and implementation.
When does code generation work well, and when does it struggle?
Code generation excels at scaffolding, routine integrations, and generating tests or helper code. It struggles with last‑mile integration, domain-specific edge cases, and complex stateful flows—areas that need design judgment and nuanced refactoring. Combining generated output with deliberate human review and iterative refinement yields the best results.
What security and configuration practices should be in place?
Lock down environment variables, keys, and secrets; use dedicated secrets managers and least-privilege access for agents and CI. Enforce code review gates for sensitive changes and require signed commits or approvals. Include security checks in CI and run static analysis as part of agent verification to catch common misconfigurations early.
How can teams measure success when shifting to an async-first, vibe-driven process?
Track time-to-feedback, deployment frequency, mean time to recovery (MTTR), and test pass rates. Measure developer flow: fewer blocking tasks, higher parallel work completion, and improved review throughput. Qualitative signals—developer satisfaction and clearer specs—also indicate progress. Use these metrics to iterate on prompts, tooling, and workflows.
