Project Agora: Building an Autonomous Software Agency on Google Cloud

The promise of AI agents is to automate complexity, but as engineering leaders, we often confront a foundational truth: a single agent, like a single junior developer, has its limits.

A simple Q&A bot can fetch documentation, but it cannot replicate the intricate, multi-step, collaborative process of real-world software development. To solve this, we don't need better prompts; we need better systems.

A true development lifecycle isn't one-shot. It's a stateful journey of analysis, research, design, feedback, generation, and — most critically — quality assurance.

This is the story of Project Agora, a framework for creating and orchestrating hierarchical multi-agent systems, built on the Google Agent Development Kit (ADK).

The Architecture: A Hierarchical Agent Topology

Project Agora implements a powerful architectural pattern: a central orchestrator_agent that manages a team of specialized sub-agents. Each specialist is wrapped in an ADK AgentTool, allowing the orchestrator to delegate complex tasks as if it were calling a simple function.

This modular design is the key to creating sophisticated, end-to-end workflows.

The Tech Stack: Why Google Cloud?

When building an autonomous agency, you need more than just an LLM; you need a resilient state machine and a scalable memory backend. Here is the production stack that powers Agora:

Orchestration: Determinism over Creativity

LLMs are non-deterministic stochastic parrots. To build a reliable coding agency, I needed strict guardrails.

The Brain: Gemini 2.5

For an autonomous coding agency, context is everything. Gemini 2.5 Pro's massive context window allows the ticket_analysis_agent to ingest entire log files or code snippets without aggressive chunking, reducing the "lost in the middle" hallucination risks common in smaller models.

Memory & Knowledge: BigQuery Vector Search

Many architects instinctively reach for Pinecone or Milvus for vector storage. I chose BigQuery Vector Search to minimize architectural complexity.

Grounding: Vertex AI RAG Engine

For the "RAG Agent," we need factual correctness, not creativity. The Vertex AI RAG Engine provides managed indexing of the ADK documentation, ensuring the agents don't hallucinate API methods that don't exist.

Infrastructure: Cloud Run

The entire agency runs as a stateless container on Cloud Run. This ensures that our "virtual employees" scale down to zero (costing nothing) when no tickets are being processed and scale up instantly when work arrives.

The Workflow: An ADK Expert System

Here is the workflow our pre-configured "ADK Expert" system follows:

Step 3: The Human-in-the-Loop

This is where the system's collaborative nature becomes visible. For a code generation request, the code_generator_agent doesn't just start writing code. It first creates a high-level plan and, using a custom tool powered by Playwright, dynamically generates a visual architecture diagram.

The orchestrator then presents both the plan and the diagram to the user for explicit approval. The system doesn't assume; it proposes and aligns.

Step 4: The QA Gate

This is the most critical loop, moving beyond simple generation into reliable automation.

Only after passing this automated quality gate is the final, verified code presented to the user.

Three Pillars of Production-Ready Agentic Systems

Building a demo is easy; building a system that doesn't crash in production is hard. Project Agora's success relies on three architectural decisions:

• State-Driven Orchestration (The Guardrails)

  We cannot treat AI as a black box. By enforcing a strict state-machine workflow within the ADK orchestrator, we ensure the system creates a "human-in-the-loop" checkpoint before any code is written.

• Integrated Quality Assurance (The Second Pair of Eyes)

  In traditional software teams, no code merges without review. The dedicated code_reviewer_agent does not generate; it critiques. It strictly enforces the Google Style Guide and security best practices.

• Infrastructure-as-Code (The One-Command Setup)

  Using a master setup_environment.sh script, I automated the provisioning of GCS buckets, BigQuery datasets, and Vertex AI indices. Agentic systems can be treated as standard infrastructure assets — versioned, reproducible, and deployable in minutes.

The Real Product: A Domain-Agnostic Framework

While the ADK expert system is a powerful demonstration, the true product here is the domain-agnostic framework itself. The entire state-driven, interactive workflow — from analysis and visualization to generation and review — is portable.

By changing the knowledge files (the RAG corpus and BigQuery tables) and the specialist agent prompts, you could transform this from a developer tool into an agentic system that automates financial reporting, legal research, or marketing campaign creation.