Building ConsultingCore: Autonomous AI Agent Development in Action

August 2, 2025

TL;DR: Executive Summary

We’re conducting a live experiment: building an AI agent orchestration platform using autonomous AI agents as our development team. After completing Phase 1 of ConsultingCore, we’ve successfully implemented core backend APIs, WebSocket services, and agent orchestration infrastructure entirely through agent-driven development. This post reveals our technical approach, key learnings, and the surprising effectiveness of agent-based software development at scale.

Key Accomplishments: Complete backend architecture with Chat, Agent, and Session APIs • Real-time WebSocket communication • Agent discovery and orchestration systems • Session management with 30-day retention • TDD-driven development process managed by specialized agents

The Experiment: AI Agents Building AI Agents

What happens when you give AI agents the task of building an AI agent platform? We’re finding out through ConsultingCore - our ambitious experiment in autonomous software development using Claude Code’s sub-agent architecture.

ConsultingCore aims to be the “ChatGPT for business agents” - a professional interface where organizations can interact with customizable AI agents for specific business functions. But here’s the twist: we’re building it entirely with autonomous AI agents managing every aspect of development.

Our Agent-Driven Architecture in Practice

The Development Team Structure

Instead of human developers, our team consists of specialized AI agents, each defined by markdown specifications that outline their responsibilities, tools, and decision-making frameworks:

---
name: engineering-backend-api-agent
description: Develops and maintains backend API services
tools: Read, Write, WebSearch
---

Our development roster includes:

Real-World Agent Coordination

The magic happens in how these agents coordinate. When we needed to implement the chat API, here’s how the workflow unfolded:

  1. Task Decomposition: The orchestration agent analyzed requirements and created specific tickets
  2. Technical Design: The backend agent architected the REST endpoints and data models
  3. TDD Implementation: The TDD coordinator ensured proper test coverage before code implementation
  4. Quality Validation: Multiple agents reviewed the implementation for security, performance, and maintainability

Phase 1 Technical Achievements

Robust Backend Infrastructure

Our agents successfully implemented a production-ready backend architecture using modern TypeScript and Fastify:

// Agent-developed chat endpoint with comprehensive error handling
fastify.post<ChatSendRequest>('/send', {
  schema: chatSendSchema,
  handler: async (request: FastifyRequest<ChatSendRequest>, reply: FastifyReply) => {
    const { agentId, message, sessionId, context } = request.body;
    
    // Agent validation and execution
    const agentExists = await agentExecutor.validateAgent(agentId);
    if (!agentExists) {
      return reply.status(404).send({
        success: false,
        error: `Agent not found: ${agentId}`,
        timestamp: new Date()
      });
    }

    // Execute with comprehensive monitoring
    const result = await agentExecutor.executeAgent(executionRequest);
    // ... additional implementation
  }
});

Advanced Agent Orchestration

The AgentExecutor class demonstrates sophisticated agent management with concurrent execution limits, performance metrics, and timeout handling:

export class AgentExecutor {
  private executionMetrics: Map<string, ExecutionMetrics> = new Map();
  private concurrentExecutions: Set<string> = new Set();

  async executeAgent(request: AgentExecutionRequest): Promise<AgentExecutionResult> {
    // Enforce concurrency limits
    if (this.concurrentExecutions.size >= config.agent.maxConcurrentAgents) {
      return {
        success: false,
        error: 'Maximum concurrent agent executions reached'
      };
    }

    // Execute with comprehensive monitoring and error recovery
    const result = await this.executeWithTimeout(agent, request, config.agent.maxExecutionTime);
    this.updateExecutionMetrics(request.agentId, executionTime, true, result.toolsUsed);
    
    return result;
  }
}

Real-Time Communication Layer

Our agents implemented WebSocket infrastructure supporting streaming responses, typing indicators, and real-time session management - features typically requiring weeks of development completed in agent-coordinated sprints.

Development Methodology: TDD at Agent Scale

Agent-Managed Test-Driven Development

One of our most successful innovations is having dedicated agents manage the TDD cycle. The engineering-tdd-coordinator enforces Red-Green-Refactor patterns across all development:

  1. Red Phase: Agents write failing tests before implementation
  2. Green Phase: Minimal code to pass tests
  3. Refactor Phase: Code quality improvement while maintaining test coverage

This approach has resulted in 90%+ test coverage and remarkably clean, maintainable code - proving that agents can excel at disciplined development practices.

Quality Gates and Code Review

Each commit goes through automated agent review covering:

Key Insights from Agent-Driven Development

What Works Exceptionally Well

Consistency and Standards: Agents never deviate from established coding standards, creating remarkably uniform codebases.

Comprehensive Documentation: Every function, API endpoint, and architectural decision gets documented without human prompting.

Error Handling: Agents implement defensive programming patterns consistently, resulting in robust error handling across the entire application.

Test Coverage: The TDD coordinator agent ensures comprehensive test coverage that human teams often struggle to maintain under deadline pressure.

Surprising Challenges

Context Switching Overhead: Agents sometimes lose context between related tasks, requiring sophisticated context management strategies.

Creative Problem-Solving: While excellent at implementing well-defined patterns, agents require clear guidance for novel architectural decisions.

Integration Complexity: Coordinating multiple agents working on interdependent features requires careful orchestration to avoid conflicts.

Technical Architecture Deep Dive

Monorepo Structure with Agent Specialization

consulting-core/
├── apps/
│   ├── frontend/          # React + TypeScript + Vite
│   └── backend/           # Node.js + Fastify + Socket.IO
├── packages/
│   ├── shared-types/      # TypeScript definitions
│   ├── agent-sdk/         # Agent integration SDK
│   └── ui-components/     # Shared React components
├── agents/                # Git submodule to agent definitions
└── .claude/               # Agent orchestration specs

Each directory maps to specific agent responsibilities, enabling parallel development without conflicts.

Agent Integration SDK

Our agents developed a sophisticated SDK for loading and executing business agents dynamically:

export class AgentLoader {
  static loadAgent(agentId: string): AgentDefinition {
    // Dynamic agent loading from markdown definitions
    const agentPath = path.join(AGENTS_PATH, `${agentId}.md`);
    const content = fs.readFileSync(agentPath, 'utf-8');
    return this.parseAgentDefinition(content);
  }

  static validateAgent(agent: AgentDefinition): boolean {
    // Comprehensive validation including tool permissions,
    // prompt safety, and execution constraints
    return this.validateSchema(agent) && 
           this.validateTools(agent) && 
           this.validatePromptSafety(agent);
  }
}

Performance Metrics and Business Impact

Development Velocity

Agent Efficiency Metrics

Our monitoring shows interesting patterns in agent performance:

Next Steps: Scaling the Agent Development Model

Phase 2: Frontend Development with Agent Teams

We’re now deploying specialized frontend agents to build the React interface, including:

Advanced Agent Capabilities

Upcoming enhancements include:

Lessons for the Future of Software Development

Implications for Development Teams

This experiment suggests several transformative possibilities:

Augmented Development: Human developers focusing on creative problem-solving while agents handle implementation and maintenance.

Quality Consistency: Agent-enforced standards resulting in dramatically improved code quality and maintainability.

24/7 Development Cycles: Continuous progress without the constraints of human work schedules.

Knowledge Preservation: Organizational development knowledge embedded in agent specifications rather than dependent on individual team members.

Enterprise Adoption Considerations

For organizations considering agent-driven development:

Forward-Looking Insights

The ConsultingCore experiment demonstrates that autonomous AI agents can successfully manage complex software development projects when properly orchestrated. We’re seeing the emergence of a new development paradigm where human creativity and strategic thinking combine with agent execution and quality enforcement.

Our next phase will test whether this model scales to complete application development, including frontend implementation, deployment automation, and ongoing maintenance. The early results suggest we’re witnessing the beginning of a fundamental shift in how software gets built.

As we continue this experiment, we’re documenting every insight, challenge, and breakthrough. The future of software development may well be written by the very agents we’re building today.

Research Sources and Methodology

  1. ConsultingCore Codebase Analysis: Direct examination of agent-generated code, commit history, and development metrics
  2. Claude Code Documentation: Anthropic’s official sub-agent architecture guidelines
  3. Agent Performance Monitoring: Real-time execution metrics, error rates, and collaboration success rates
  4. Industry Development Standards: Comparison with traditional development velocity and quality metrics
  5. TDD Research: Academic studies on test-driven development effectiveness and automation
  6. Software Architecture Patterns: Analysis of monorepo structures and microservice orchestration in enterprise environments

The methodology combines quantitative performance metrics with qualitative analysis of agent collaboration patterns, providing comprehensive insights into the effectiveness of autonomous development teams.

This post is part of our ongoing series documenting the ConsultingCore experiment. Follow our progress as we push the boundaries of what’s possible with autonomous AI development teams.