I’m going to show you why Google ADK’s Quick Start is a trap, what most people miss, and how to build something that doesn’t implode the moment you ship it.
First — What’s Actually in the Quick Start?
Here’s what the Quick Start gives you:
- A single-file Python script
- A couple of toy tools (get_time, get_weather)
- An agent that reasons using a hardcoded prompt
- A memory object that magically persists across turns
- Zero validation, zero error handling, zero logs
It’s cute and clever, but it’s not real architecture. It’s a proof of concept, not a pattern.
Let’s break down what you’ll hit the moment you try building anything more complex:
1. No Real Memory Management
The Quick Start uses AgentContext(). Put () to store data and magically recall it later.
But it doesn’t teach you:
- How to scope memory per user
- How to clear or erase memory
- How to encrypt or sanitize PII
- How to track what’s being remembered
This is how agents start leaking user data in prod — silently and invisibly.
2. Zero Tool Permissions
In the Quick Start, tools are global. The agent can access anything.
In the real world?
- You need an auth-based tool access.
- Some tools should be context-specific.
- Others should be revoked or replaced at runtime.
Right now, your agent can call the wrong tool , and there’s no sandbox to stop it.
3. Hardcoded Prompt Logic
The Quick Start builds its reasoning off one prompt template.
In reality:
- Prompts need dynamic slot-filling.
- You’ll need prompt versioning.
- Multi-step reasoning requires flow-aware chaining.
If you update that one prompt, the entire agent behavior can shift. There’s no safety net.
4. No Error Handling, Retry Logic, or Tool Failover
What happens when
- A tool times out?
- A model returns garbage?
- A required context field is missing?
In the Quick Start? It just fails silently. No retries, logs, fallback, or resilience.
5. No Testing Strategy
The demo runs once and prints to the console.
Real systems need:
- Unit tests for tools
- Regression tests for prompts
- Snapshot tests for agent output
- Agent mocks for CI/CD.
If your agent changes behavior and no test catches it… Did it even happen?
The trap isn’t that it’s simple. The trap is that it hides how complex agents are. And when you realize it too late, after you’ve promised a product, you’re stuck duct-taping around a brittle core.
What You Need to Build
Let’s say you’re serious. You want to build a production-grade agent system with Google ADK. Here’s your real TODO list:
1. A Memory Layer That Respects Boundaries
- Per-session or per-user scoped
- Memory expiration logic
- Redaction and sanitization
- Optional vector search integration
2. A Tool Registry With Auth + Metadata
- Who can call what (RBAC)
- Tool schemas with validation
- Failover tools for redundancy
- Logging + error capture per tool call
3. Structured Logging + Tracing
- Tool execution logs
- Prompt + response storage
- Chain-of-thought debugging
- OpenTelemetry/Sentry hooks
4. Prompt Versioning + Safety Nets
- Prompt template with placeholders
- Prompt changelogs + rollback
- Test suites for prompt regressions
5. Testing + CI Integration
- Unit tests for tools and logic
- Agent behavior snapshots
- Simulated tool failures
- CI/CD with test gates before shipping
No comments:
Post a Comment