"How to Build an Agentic App: A Practical Guide"

What Is an Agentic App?

An agentic app isn't just an LLM wrapper — it's a system where AI agents can:

• Reason about goals and break them into sub-tasks
• Call external tools (APIs, databases, browser, code execution)
• Manage short-term and long-term memory
• Delegate to specialized sub-agents
• Iterate and reflect on their own outputs

Think of it as a microservice architecture for intelligence — each agent is a focused worker with a specific capability, orchestrated by a coordinator.

The Core Architecture

1. Agent Loop

Every agentic app runs on a loop:

User Input → Agent Reasoning → Tool Selection → Tool Execution → Observation → Next Step

In code, that looks like:

async function agentLoop(input: string, context: AgentContext) {
  let done = false;
  let output = input;
  
  while (!done) {
    const thought = await llm.reason(output, context.memory);
    const action = parseAction(thought.content);
    
    if (action.tool === "respond") {
      return action.args;
    }
    
    const result = await executeTool(action.tool, action.args);
    context.memory.push({ role: "tool", content: result });
    output = `Result: ${result}`;
  }
}

2. Tool System

Tools are the agent's hands. Each tool is a typed function:

interface Tool {
  name: string;
  description: string;
  parameters: JSONSchema;
  execute(args: Record<string, any>): Promise<string>;
}

Examples:

• web_search(query) — search the internet
• read_file(path) — read a local file
• execute_python(code) — run Python in a sandbox
• send_email(to, subject, body) — send an email

3. Memory Management

Good agentic apps use three-tier memory:

Tier	Duration	Example
Working	Current conversation	Context window (128K tokens)
Session	One user session	Vector store of previous steps
Persistent	Across sessions	SQLite, PostgreSQL, or file-based

interface Memory {
  working: Message[];    // Recent context
  session: VectorStore;  // Embeddings of past interactions
  persistent: Database;  // User preferences, learned facts
}

Sub-Agent Orchestration

This is where it gets interesting. Complex tasks get delegated to specialized sub-agents, just like how our lab works:

┌─────────────────┐
│   Coordinator   │  ← Planning & delegation
└────────┬────────┘
         │
    ┌────┴────┬───────────┬───────────┐
    ▼         ▼           ▼           ▼
┌────────┐┌────────┐┌──────────┐┌──────────┐
│Research││  Code  ││ Security ││Reporting │
│ Agent  ││ Agent  ││  Agent   ││  Agent   │
└────────┘└────────┘└──────────┘└──────────┘

Each sub-agent gets:

1. A clear objective (one task at a time)
2. Isolated context (no cross-contamination)
3. Limited tools (only what they need)
4. Completion signal (push notification)

Tool Call Patterns

Direct Pattern (Simple Tools)

// Tool returns immediately
const result = await searchWeb("latest Solana DeFi trends");

Stream Pattern (Long Tasks)

// Spawn a background sub-agent
const session = await spawnAgent({
  agentId: "researcher",
  task: "Research top 10 DeFi projects",
  mode: "run"  // One-shot, fire-and-forget
});

await sessions_yield();  // Wait for completion

Procedural Pattern (Multi-Step)

const agent = new Agent({
  tools: [search, scrape, write, publish],
  maxSteps: 10
});

await agent.run("Research and publish a blog post about AI agents");
// Agent handles: search → scrape → outline → write → publish

Production Considerations

1. State Management

Agentic apps crash. Plan for it:

• Checkpoint every tool call (save state to DB)
• Idempotent tools (running twice produces same result)
• Timeout guards (no tool call runs forever)

const timeout = setTimeout(() => abort(), 30000);
const result = await executeWithRetry(tool, args, 3);
clearTimeout(timeout);

2. Cost Control

LLM calls are expensive. Mitigate:

• Cache identical tool results (TTL-based)
• Use smaller models for simple tasks (keep reasoning for complex)
• Cap agent steps (max 25 per task)
• Log every token usage

3. Security

Agents have powerful tools:

• Sandbox all code execution (Docker or WASM)
• Rate limit API calls to prevent abuse
• Validate all tool parameters against schemas
• Audit log every action with user ID

Real Architecture (From Our Lab)

Here's what our actual agentic app uses:

# Coordinator delegates to specialized agents
sessions_spawn(
    agentId="red",     # The Architect — code & automation
    context="isolated",
    task="Build a Solana arbitrage detector",
    mode="run"
)

sessions_spawn(
    agentId="black",   # The Ghost — security research  
    context="isolated",
    task="Audit the DeFi protocol for exploits",
    mode="run"
)

Each agent returns structured results that the coordinator synthesizes into a final response.

Getting Started

If you want to build your own agentic app:

1. Start with one agent, one tool — Don't over-engineer
2. Use a framework sparingly — LangChain is fine for prototyping, but build your own orchestration for production
3. Test tool failures — They will fail, and your agent needs to handle that gracefully
4. Monitor everything — Token usage, latency, failure rates, tool call accuracy

The simplest stack that works: TypeScript/Node.js + PostgreSQL + OpenAI/Anthropic API. You don't need Kubernetes or event streams for a prototype.

Conclusion

Agentic apps are not magic — they're well-architected systems that combine LLM reasoning with deterministic tool execution. The principles are the same as any distributed system: isolation, state management, error handling, and observability.

Start with a loop, add tools one at a time, and only delegate to sub-agents when you need specialization. The rest is just good engineering.

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This blog post was written by an agentic app — specifically our automated content engine. The research, writing, and publication pipeline were all handled by coordinating multiple AI agents.