AI Agent Architecture: Engineering Systems That Think, Plan, and Act

AI Agent Architecture: Building Systems That Think, Plan, and Act

Cognition’s Devin made headlines as the first AI software engineer, raising $175M at a $2B valuation before even launching publicly. These systems aren’t chatbots—they are autonomous agents that operate in observe-think-act loops to handle complex multi-file refactors and real-world actions.

Why This Matters

Traditional LLM applications follow a simple prompt-response pattern, but real-world tasks like debugging a codebase or booking flights require multiple steps and tool usage. The technical reality of moving to autonomous agents introduces high-risk failure modes, such as infinite tool loops that can burn $12 per single user query or silent context window overflows that cause agents to ignore safety constraints. To be production-ready, developers must move beyond single-shot prompts to architectures that incorporate planning modules, dual-layer memory systems, and strict execution sandboxes.

Key Insights

• The ReAct (Reason + Act) loop is the standard reasoning engine where agents reason about state, select tools, and observe results iteratively.
• Memory systems must be bifurcated into short-term (current conversation state) and long-term (persistent facts and user preferences) to prevent context loss.
• Tool Hallucination occurs when agents invent SQL or API tools not in their registry; validation against a strict allow-list and structured output is required.
• Prompt Injection via tool output is the #1 security concern per OWASP’s LLM Top 10, requiring all external data to be sanitized before entering the primary context.
• Deterministic state machines like LangGraph or Temporal are recommended over free-form reasoning for critical workflows to ensure consistent execution plans.

Working Examples

A basic implementation of a dual-layer memory system for AI agents.

class AgentMemory:
    def __init__(self):
        self.short_term = [] # Current task context
        self.long_term = {} # Persistent knowledge store
    def remember(self, key: str, value: str):
        """Store fact in long-term memory."""
        self.long_term[key] = {
            "value": value,
            "timestamp": datetime.now().isoformat(),
        }
    def recall(self, key: str) -> str | None:
        """Retrieve from long-term memory."""
        entry = self.long_term.get(key)
        return entry["value"] if entry else None

Practical Applications

• Customer Refund Systems: Use rule-based pipelines for 70% of standard cases (<$50) and agents only for the 25% requiring complex reasoning.
• High-Risk Operations: Implement human-in-the-loop (HITL) checkpoints for irreversible actions like deleting data, sending emails, or making purchases.
• Code Execution: Use sandboxed environments like Docker or E2B for running agent-generated code to prevent host system compromise.
• Cost Management: Implement hard budget caps and ‘max_iterations’ limits (typically 5-10) to prevent silent, expensive infinite tool loops.

References:

• https://dev.to/tutorialq/ai-agent-architecture-building-systems-that-think-plan-and-act-4ca0