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# Introduction
Agentic AI is becoming extremely popular and relevant across industries. But it also represents a fundamental shift in how we build intelligent systems: agentic AI systems that break down complex goals, decide which tools to use, implement multi-stakeholder plans, and adapt when things go wrong.
When building such agentic AI systems, engineers are designing decision-making architectures, implementing safety barriers that prevent failures without killing resilience, and building feedback mechanisms that help agents recover from mistakes. The technical depth required is significantly different from traditional AI development.
Agentic AI is still new, so experience is very important. Definitely look for candidates who have built practical agentic AI systems and can discuss trade-offs, explain failure modes, and justify their design choices with real reasoning.
How to use this article: This collection focuses on questions that test whether candidates really understand the agent system or just know the buzzwords. You will find questions on tool integration, planning strategies, error handling, security design and more.
# Building agentic AI projects
When it comes to projects, quality beats quantity every time. Don’t build ten half-baked chatbots. Focus on building an agentic AI system that actually solves a real problem.
So what makes a project an “agent”? Your project should demonstrate that an AI can operate with some autonomy. Think: planning multiple actions, using tools, making decisions, and recovering from failures. Try to construct projects that demonstrate understanding:
- Personal Research Assistant – takes a question, searches multiple sources, synthesizes results, asks clarifying questions
- Code Review Agent – Analyzes pull requests, runs tests, suggests improvements, explains its rationale
- Data Pipeline Builder – Understands requirements, designs schema, generates code, validates results
- Meeting Prep Agent – gathers context about participants, pulls relevant documents, creates agenda, suggests talking points
What to emphasize:
- How your agent breaks down complex tasks
- What tools does it use and why?
- How it handles errors and ambiguities
- Where you’ve given him autonomy vs. constraints
- It solves real problems (even if only for you).
A solid project with thoughtful design choices will inspire you more — and more — than a portfolio of tutorials.
# Core agentic concepts
// 1. What defines an AI agent and how does it differ from a standard LLM application?
What to focus on: Understanding autonomy, goal-directed behavior, and multidimensional reasoning.
Answer along these lines: “An AI agent is an autonomous system that can understand and interact with its environment, make decisions, and take actions to achieve specific goals. Unlike standard LLM applications that respond to single cues, agents maintain state in interactions, plan multistep workflows, and change their approach based on feedback.”
🚫 Avoid: Confused agents with simple tool calling, don’t understand the autonomous aspect, miss the goal oriented nature.
You can also refer What is Agentic AI and how does it work? And Generative AI vs. Agentic AI vs. AI Agent.
// 2. Describe the main architectural patterns for building AI agents
What to focus on: Knowledge of reactive, plan-oriented, and multi-agent architectures.
Answer along these lines: “Reaction (reasoning + acting) alternates between reasoning steps and execution, making decisions observable. Planning-based agents prepare complete action sequences, then better for complex, predictable tasks.”
🚫 Avoid: Knowing only one pattern, not understanding when different approaches are used, lose trades.
If you’re looking for a comprehensive resource on agent design patterns, check out Choose a design pattern for your agentic AI system by Google And Agentic AI Design Patterns Introduction and Walkthrough by Amazon Web Services.
// 3. How do you handle state management in long-running agent workflows?
What to focus on: An understanding of persistence, context management, and failure recovery.
Answer along these lines: “Implement explicit state storage with versions of workflow progress, intermediate results, and decision history. Use checkpoints at critical workflow steps to enable recovery. To maintain both short-term context (current task) and long-term memory (learned patterns).
🚫 Avoid: Relying only on conversation history, not considering failure recovery, missing the need for clear state management.
# Tool integration and orchestration
// 4. Design a robust tool calling system for the AI ​​agent
What to focus on: Error handling, input validation, and scalability considerations.
Answer along these lines: “Implement tool schemes with strict input validation and type checking. Use ASYNC execution with timeout to avoid blocking. Retry with explicit backoff for transient failures.”
🚫 Avoid: Not considering error cases, missing input validation, not scalability planning.
look Tool calling isn’t just plumbing for AI agents – Roy Dirks To understand how to implement tool calling in your agent applications..
// 5. How will you handle tool calling failures and partial results?
What to focus on: graceful degradation strategies and error recovery mechanisms.
Respond along the lines of: “Implement tiered fallback strategies: retry with different parameters, use alternative tools, or actively harass functionality. Design for partial results, restarting from intermediate states to continue the design.”
🚫 Avoid: Retry-only strategy, not planning for partial results, incremental path loss.
Depending on the framework you are using to build your application, you may refer to specific documentation. For example, How to handle tool calling errors The Lang Graph framework covers such error handling.
// 6. Explain how you will create a tool discovery and selection system for agents
What to focus on: Dynamic tool management and intelligent selection strategies.
Respond along the lines of: “Create a tool registry with terminology, capabilities metadata, and usage examples. Implement tool ranking based on task requirements, past success rates, and current availability. Use similarity matching for tool discovery based on natural language descriptions. Include cost and latency considerations in selection. Design
🚫 Avoid: Hard-coded tool lists, no selection criteria, lacks dynamic discovery capabilities.
# Planning and reasoning
// 7. Compare different planning methods for AI agents
What to focus on: Understanding hierarchical planning, reactive planning, and hybrid approaches.
Answer along these lines: “Hierarchical planning breaks complex goals into subgoals, which enables better organization but requires a well-decomposed strategy. Reactive planning responds to immediate conditions, offering flexibility but potentially missing optimal solutions.” Monte Carlo Tree Search Explores process spaces systematically but requires good evaluation functions. Hybrid approaches use high-level planning with reactive implementation. The choice depends on task predictability, time constraints and environmental complexity. “
🚫 Avoid: Knowing only one perspective, not considering the specifics of the work, missing the trade-off between depth of planning and speed of execution.
// 8. How do you implement efficient goal decomposition in an agent system?
What to focus on: Strategies for breaking down complex goals and dealing with dependencies.
Answer along the lines of: “Use an iterative goal decomposition with clear success criteria for each subgoal. Implement dependency tracking to manage the order of execution. Include goal prioritization and resource allocation. Design goals to be specific, measurable, and time-bound. Use templates for shared goal patterns. Include conflicts for conflict resolution.”
🚫 Avoid: ad hoc decomposition without structure, not handling dependencies, missing context.
# Multi-Agent System
// 9. Design a multi-agent system to solve the cooperation problem
What to focus on: Communication protocols, coordination mechanisms, and conflict resolution.
Respond along the lines of: “Define special agent roles with clear capabilities and responsibilities. Implement message-passing protocols with structured communication formats. Use coordination mechanisms such as task auctions or consensus algorithms. Include conflict resolution processes for conflict resolution.”
🚫 Avoid: Unclear role definitions, no coordination strategy, missing conflict resolution.
If you want to learn more about building multi-agent systems, work Multi-AI Agent System with Cryoi by Deploying.AI.
# Safety and reliability
// 10. What security mechanisms are necessary for production agent AI systems?
What to focus on: Understanding containment, monitoring and human supervision requirements.
Respond along the lines of: “Implement action sandboxing to limit agent capabilities to approved actions. Use authorization systems to require explicit authorization for sensitive actions. Include monitoring for unusual behavior patterns. Design kill switch for immediate agent shutdown.”
🚫 Avoid: No containment strategy, no human supervision, no consideration of counter-scenarios.
To know more, read on Deploying Agentic AI with Safety and Security: A Playbook for Technology Leaders McKinsey Report.
# wrap up
Agentic AI engineering calls for a unique combination of AI expertise, systems thinking, and security awareness. These questions probe the practical knowledge needed to build autonomous systems that work reliably in production.
The best agent AI engineers design systems with appropriate safeguards, clear observability, and graceful failure modes. They think beyond single interactions to complete workflow orchestration and long-term system behavior.
Would you like us to do a sequel with more relevant questions on agentic AI? Tell us in the comments!
Bala Priya c is a developer and technical writer from India. She loves working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include devops, data science, and natural language processing. She enjoys reading, writing, coding and coffee! Currently, she is working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces and more. Bala also engages resource reviews and coding lessons.