Agentic AI with Google ADK: Building Autonomous Agents That Actually Work

The AI industry’s hottest buzzword is “agentic.” Every startup claims to build agents. Most of them are glorified chatbots with a for-loop.

Real agents make decisions, use tools, handle failures, and know when to ask for help. Here’s how I build them at Samsung SDS using Google’s Agent Development Kit (ADK).

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What Makes an Agent “Agentic”?

An LLM wrapper that calls an API is not an agent. An agent has:

1. Autonomy — It decides what to do next, not just follows a script
2. Tool use — It can interact with external systems (APIs, databases, file systems)
3. Memory — It maintains context across interactions
4. Self-correction — It recognizes failures and adapts its strategy
5. Boundaries — It knows when to stop and escalate to a human

If your “agent” is just while True: response = llm.call(prompt), that’s a loop, not an agent.

Why Google ADK?

After evaluating LangChain, LangGraph, CrewAI, and AutoGen, I chose Google ADK for production work because:

• First-class tool definition — Type-safe tool schemas with automatic validation
• Built-in guardrails — Input/output validators, content safety filters
• MCP protocol support — Interoperable with any MCP-compatible tool server
• A2A protocol — Agent-to-Agent communication for multi-agent systems
• Evaluation framework — Built-in metrics for agent quality assessment

The ecosystem is young but production-focused — exactly what Samsung SDS needed.

Pattern 1: Tool-Use Agent

The simplest pattern — an agent that can use tools to accomplish tasks:

from google.adk import Agent, Tool

@Tool
def search_knowledge_base(query: str) -> str:
    """Search the enterprise knowledge base."""
    results = vector_db.similarity_search(query, k=5)
    return format_results(results)

@Tool  
def send_email_report(to: str, subject: str, body: str) -> str:
    """Send an automated email report."""
    smtp_client.send(to=to, subject=subject, body=body)
    return f"Email sent to {to}"

agent = Agent(
    model="gemini-2.0-flash",
    tools=[search_knowledge_base, send_email_report],
    system_prompt="You are an enterprise knowledge assistant..."
)

The key insight: tool descriptions matter more than the system prompt. The LLM decides which tool to call based on the tool’s docstring, not your instructions.

Pattern 2: Multi-Step Agent with Self-Evaluation

This is the pattern I use for our RAG chatbot. The agent doesn’t just retrieve and respond — it evaluates its own answer quality:

agent = Agent(
    model="gemini-2.0-flash",
    tools=[search_kb, search_graph, evaluate_response],
    planning_strategy="react",  # ReAct: Reason + Act
    max_steps=5,
    guardrails=[
        ContentSafetyGuardrail(),
        ConfidenceThresholdGuardrail(min_confidence=0.7),
    ]
)

The evaluate_response tool scores the agent’s own output on relevance, faithfulness, and completeness. If the score is below threshold, the agent automatically reformulates the query and tries again.

This self-correction loop is what separates a production agent from a demo.

Pattern 3: Multi-Agent Orchestration

For complex workflows, I use multiple specialized agents coordinated by a supervisor:

• Retriever Agent — Finds relevant documents from vector DB + knowledge graph
• Analyzer Agent — Extracts structured data from retrieved documents
• Reporter Agent — Generates formatted reports and sends via email

Each agent has its own tools and system prompt. The supervisor decides which agent to invoke based on the user’s request.

MCP Integration: The Game Changer

The Model Context Protocol (MCP) standardizes how agents interact with tools. Instead of writing custom tool wrappers for every API, you connect to MCP servers:

agent = Agent(
    model="gemini-2.0-flash",
    mcp_servers=[
        "database://internal-knowledge-base",
        "api://jira-project-tracker",
        "filesystem://shared-reports",
    ]
)

One protocol, any tool. This is how we connect our agents to JIRA, Confluence, email, and internal databases without writing boilerplate.

Guardrails: Non-Negotiable in Enterprise

Production agents without guardrails are ticking time bombs. What I implement on every agent:

1. Input sanitization — Block prompt injection attempts
2. Output validation — Ensure responses match expected format
3. Content safety — Filter harmful or inappropriate content
4. Rate limiting — Prevent runaway agent loops
5. Human escalation — Automatic handoff when confidence drops below threshold
6. Audit logging — Every tool call and decision logged for compliance

At Samsung SDS, enterprise compliance isn’t optional. Every agent interaction is logged, every tool call is auditable.

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Lessons Learned

After building 4 production agents at Samsung SDS:

1. Start simple — A single-tool agent that works reliably beats a multi-agent system that fails randomly
2. Tool quality > Agent sophistication — The agent is only as good as its tools
3. Evaluation is continuous — We run agent evaluation suites weekly, not just at launch
4. Guardrails first, features second — Build the safety layer before the capability layer
5. MCP is the future — Standardized tool interfaces save months of integration work

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Building agents? I share architectures, patterns, and real-world failures on LinkedIn. Workshop materials from my INCEPT’26 talk are on GitHub.