How 85+ AI Agents Build Software in Parallel

Anton · CTO & Co-Founder, OneChair

At 9:00 AM, a client described their project. A scheduling and resource management platform for a professional services firm — multi-project allocation, utilization dashboards, capacity forecasting, integration with their existing project management tools. By 5:00 PM that day, ten screens were live on a staging URL with real data flowing through them.

This is not exceptional performance. It is what parallel execution at scale looks like when it is working correctly. To understand why it is possible, you need to understand what is actually happening inside the agent network — and what it is not doing.

Meet the Agents — Specialized AI for Every Task

The core insight behind AI-orchestrated development is that specialization matters as much for AI agents as it does for human engineers. A general-purpose AI asked to simultaneously design a database schema, write a React component, create a test suite, and draft documentation produces mediocre output across all four. A specialist agent focused entirely on PostgreSQL schema design, with deep context about the project's data relationships and constraints, produces excellent output in that specific domain.

OneSpark deploys over 85 agents across six functional categories:

Scoping and Architecture Agents

These agents operate before any code is written. Scoping agents parse the requirements document, identify unstated dependencies (if you need multi-provider scheduling, you also need conflict resolution logic — even if that was not written in the spec), and produce a structured specification that every downstream agent references. Architecture agents design the system topology: service boundaries, API contracts, data flow, and integration points. Their output is not prose documentation — it is structured artifacts that other agents consume directly.

Design Agents

Design agents translate the architecture into component hierarchies and screen layouts. They do not produce static mockups — they produce the component structure that frontend agents implement. Design agents are aware of the project's design system and apply it consistently across every screen. They flag inconsistencies (a form that does not follow the established input pattern) before a human would need to catch them in a review.

Frontend Agents

Each major section of the frontend application has a dedicated agent. The dashboard agent is not the same agent building the booking flow. Each agent has full context for its domain, the approved component hierarchy, and the API contracts it will call. Frontend agents write typed code — TypeScript, not JavaScript — which means interface mismatches between components are caught at the type level before runtime. They generate the components, wire up state management, implement routing, and handle error and loading states for every data-fetching view.

Backend Agents

Backend agents implement the service layer according to the API contracts defined during planning. Each endpoint has a specification that includes input validation rules, expected response shapes, error codes, and authorization requirements. Backend agents implement those specifications and do not deviate from them — which ensures the frontend and backend are always building toward the same interface. Separate agents handle the database layer, caching, background job processing, and third-party integrations.

Testing Agents

Testing agents run in parallel with the code they test, not after it. This is a meaningful distinction. In traditional development, QA happens at the end when the pressure to ship is highest and the appetite for defect-driven delays is lowest. In AI-orchestrated development, tests are written as the code is written, and they run continuously. Unit tests verify individual functions. Integration tests verify that the frontend and backend are actually speaking the same language. End-to-end tests simulate the user flows that matter most. The test suite is not an afterthought — it is part of the deliverable.

Documentation Agents

Documentation agents generate technical documentation from the code as it is committed. Every API endpoint has documentation. Every component has a usage guide. The deployment runbook is generated and verified against the actual infrastructure configuration. Documentation that is written after the code is done is documentation that gets skipped under schedule pressure. Documentation generated as a parallel artifact does not compete with the delivery timeline.

Parallel Execution vs Sequential — Why It Changes Everything

To understand the time difference, consider how the same project unfolds in each model.

In a traditional sequential development process, a 10-week project might break down roughly like this: requirements and planning take two weeks, backend development takes three weeks, frontend development takes three weeks (waiting for the backend API to be stable before building against it), QA takes one week, and documentation and deployment take one week. The total is constrained by the longest sequential chain — you cannot finish frontend before you start it, and you cannot start it before the backend API is stable enough to build against.

In the parallel model, requirements and architecture are completed in hours, not weeks. Backend and frontend agents begin building simultaneously — frontend agents build against the API contract specification, not a live backend, so they do not need to wait. Testing agents write tests in parallel with code. Documentation agents document in parallel with everything else. The sequential dependencies that consume weeks in traditional development are resolved by the architecture contract approach: agents agree on interfaces before building, then build simultaneously toward those agreed-upon interfaces.

The result is not just faster — it is structurally different. Traditional development compresses time by adding more people (with diminishing returns due to coordination overhead). AI-orchestrated development compresses time by removing the sequential bottleneck. More agents do not add coordination overhead the way more humans do, because agents operate from structured contracts rather than informal communication.

The speed is not a product of working faster. It is a product of working simultaneously. Parallel execution at scale collapses timelines that sequential processes cannot compress regardless of team size.

Jarrett Dargusch, OneChair

Quality Controls — Why AI Needs Human Oversight

The question we hear most often: if AI agents are building everything, who is checking the work?

The answer is: other agents, and then senior engineers. Both are necessary.

Review agents operate as a quality layer above the implementation agents. Every piece of code committed to the repository has been reviewed by a review agent before it lands. Review agents check for adherence to the project's coding standards, verify that error handling covers the specified edge cases, confirm that authorization checks are in place on every protected endpoint, and flag patterns that suggest technical debt. This is automated review that runs faster than a human code review and catches the mechanical errors that human reviewers sometimes miss because they are reading too quickly.

Security agents run static analysis against the codebase throughout the build. They check for common vulnerability patterns — SQL injection risks, authentication bypass opportunities, improper input sanitization, hardcoded secrets — and require remediation before work continues. This is not a post-build security audit; it is a continuous security review baked into the build process.

Type-checking is enforced at the language level. All code is TypeScript. Interface contracts between components and between the frontend and backend are encoded as types. A type error is caught at compile time, not in a production bug report. The discipline of a fully-typed codebase is one of the most reliable indicators of long-term maintainability, and it is enforced by the build process rather than by convention.

Human engineers review the complete build before delivery. This is the checkpoint that no automation can fully replace. Our senior engineers are not reading every line — they are reviewing the architecture, the data model, the security posture, the compliance implementation, and the places where agent-generated code has made choices that might seem correct in isolation but create problems in combination. The human review is focused on the judgment calls: the edge cases with real business impact, the compliance requirements with nuanced interpretations, the architectural choices that look fine today but will cause pain at scale.

Penetration testing is included on projects with security-sensitive requirements. A security engineer attempts to break the application before it ships. This is a different discipline from automated security scanning — it requires the creative adversarial thinking that finds the vulnerabilities that automated tools miss.

What the Client Experience Looks Like

The mechanics of the agent network are invisible to clients. What they experience is different from traditional development in concrete, practical ways.

From day two of the project, there is a live staging URL. The application is not finished — far from it — but it is real. You can see the navigation, the first screens, the database schema reflected in actual UI. You are not waiting until week eight to see what you paid for. You are watching the application grow in real time.

Progress updates are real. When we say 40% complete, it means 40% of the defined screens and features are live on staging. It is not a project manager's estimate of effort expended — it is a direct count of completed deliverables. Clients who have been burned by traditional development schedules ("we are 90% done" for three consecutive weeks) find this disorienting at first. The progress is visible and verifiable.

Weekly sync calls run for 20–30 minutes rather than the hour-long status meetings typical in traditional projects. The staging URL makes most status updates unnecessary — clients can see what is built. The calls focus on decisions: a requirement that turned out to have two valid interpretations, a technical constraint that affects an upcoming feature, a scope question from the client side. We are not filling time; we are resolving decisions.

Delivery is complete, not partial. When the project ships, you receive the full source code, deployment documentation, a recorded architectural walkthrough, and a 30-day support window. The documentation was written as the code was written — it is not a rushed post-project addendum. The architectural walkthrough covers how the system is structured, how the key flows work, and where to look first if you need to extend the application.

For a deeper look at how a specific project unfolded in practice, the WellChild case study walks through the build of a HIPAA-compliant healthcare platform and the Cody Yellowstone case study covers an AI-augmented platform with complex integration requirements.

Frequently Asked Questions

Do AI agents make mistakes?

Yes. AI agents produce errors that require correction — logic bugs, misread requirements, edge cases not covered by the specification. This is expected and accounted for. The quality control architecture (review agents, type enforcement, automated testing, human engineer review) is designed to catch these errors before they reach production. The error rate at the point of human review is substantially lower than in traditional development because the automated quality gates catch the mechanical errors earlier in the process. The human review focuses on the judgment-dependent errors that automation cannot reliably catch.

What happens if requirements change mid-project?

The architecture contract approach makes mid-project changes more expensive than in some traditional models — if a change affects the API contract, downstream agents need to update their implementations. For this reason, we invest significantly in the planning phase to surface ambiguities and make sure requirements are stable before build begins. Genuinely new requirements that emerge mid-project are assessed against the original scope, quoted separately, and scheduled as a follow-on build rather than integrated mid-stream. This is a deliberate constraint that protects build quality and delivery certainty.

Can clients inspect the code as it is built?

Yes. Every project uses a private GitHub repository that the client has access to from day one. You can review every commit, examine the test suite, and inspect the implementation at any point during the build. For clients with technical teams, this visibility is valuable. For clients without technical teams, the staging URL and the recorded walkthrough at delivery provide the same practical reassurance in a more accessible form.

Does the AI approach work for projects with unusual or novel requirements?

The agent network is most effective for requirements that have established patterns — SaaS platforms, booking systems, CRMs, dashboards, APIs, mobile applications. For requirements that are genuinely at the frontier of what has been built before — novel cryptographic protocols, research-grade ML systems, highly specialized embedded systems — the pattern-matching foundation of AI agents is less reliable, and traditional expert development is more appropriate. We will tell you honestly during the audit which category your project falls into. There is no value in starting a project with the wrong tool.

To understand the broader methodology behind what the agents are doing, see What Is AI-Orchestrated Development? To see the cost implications of this approach versus traditional alternatives, see our custom software service page.