Parth Agrawal

As an AI Engineer, don't just learn how to build AI agents. Learn how to make them reliable. Here are 15 things you should understand before shipping agents to production: 1. Tool orchestration: How to coordinate multiple tools, APIs, and models without creating unnecessary complexity. 2. State management: How to maintain conversation state, task progress, and intermediate results across multiple agent steps. 3. Memory architecture: Short-term memory, long-term memory, episodic memory, and when to use each. 4. Planning vs. execution: Separate task decomposition from tool execution instead of letting agents improvise every step. 5. Tool contracts: Define strict input schemas, output formats, validation rules, and failure responses for every tool. 6. Retry strategies: Implement exponential backoff, retry limits, timeouts, and circuit breakers for unreliable dependencies. 7. Idempotency: Prevent duplicate payments, repeated database writes, and unintended side effects when tools are called multiple times. 8. Human-in-the-loop: Know when an agent should pause and request human approval before taking irreversible actions. 9. Agent coordination: Understand supervisor, hierarchical, sequential, and parallel agent architectures. 10. Context management: Control context growth, summarize intermediate results, and preserve important information across long-running tasks. 11. Termination conditions: Define clear stopping criteria, execution budgets, and maximum iteration limits to prevent infinite loops. 12. Permission boundaries: Give agents only the tools and access they need. Never assume an LLM will respect security boundaries on its own. 13. Observability: Trace every model call, tool invocation, state transition, token usage, and execution failure. 14. Agent evaluation: Test task completion, tool selection, argument accuracy, recovery behavior, and consistency across multiple runs. 15. Production resilience: Design fallback models, graceful degradation, failure recovery, and rollback strategies. Building an agent that works once is easy. Building one that works reliably across thousands of unpredictable user requests is a completely different engineering problem. Stop optimizing agents for impressive demos. Start engineering them for production.

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