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# Introduction
Agentic AI is becoming super 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, execute multi-step plans, and adapt when things go wrong.
When building such agentic AI systems, engineers are designing decision-making architectures, implementing safety constraints that prevent failures without killing flexibility, 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 hands-on experience is much more important. Be sure to look for candidates who’ve built practical agentic AI systems and can discuss trade-offs, explain failure modes they’ve encountered, and justify their design choices with real reasoning.
How to use this article: This collection focuses on questions that test whether candidates truly understand agentic systems or just know the buzzwords. You’ll find questions across tool integration, planning strategies, error handling, safety design, and more.
# Building Agentic AI Projects That Matter
When it comes to projects, quality beats quantity every time. Don’t build ten half-baked chatbots. Focus on building one agentic AI system that actually solves a real problem.
So what makes a project “agentic”? Your project should demonstrate that an AI can act with some autonomy. Think: planning multiple steps, using tools, making decisions, and recovering from failures. Try to build projects that showcase understanding:
- Personal research assistant — Takes a question, searches multiple sources, synthesizes findings, asks clarifying questions
- Code review agent — Analyzes pull requests, runs tests, suggests improvements, explains its reasoning
- Data pipeline builder — Understands requirements, designs schema, generates code, validates results
- Meeting prep agent — Gathers context about attendees, pulls relevant docs, creates agenda, suggests talking points
What to emphasize:
- How your agent breaks down complex tasks
- What tools it uses and why
- How it handles errors and ambiguity
- Where you gave it autonomy vs. constraints
- Real problems it solved (even if just for you)
One solid project with thoughtful design choices will teach you more — and impress more — than a portfolio of tutorials you followed.
# Core Agentic Concepts
// 1. What Defines an AI Agent and How Does It Differ From a Standard LLM Application?
What to focus on: Understanding of autonomy, goal-oriented behavior, and multi-step reasoning.
Answer along these lines: “An AI agent is an autonomous system that can perceive and interact with its environment, makes decisions, and takes actions to achieve specific goals. Unlike standard LLM applications that respond to single prompts, agents maintain state across interactions, plan multi-step workflows, and can modify their approach based on feedback. Key components include goal specification, environment perception, decision-making, action execution, and learning from outcomes.”
🚫 Avoid: Confusing agents with simple tool-calling, not understanding the autonomous aspect, missing the goal-oriented nature.
You can also refer to What is Agentic AI and How Does it Work? and Generative AI vs Agentic AI vs AI Agents.
// 2. Describe the Main Architectural Patterns for Building AI Agents
What to focus on: Knowledge of ReAct, planning-based, and multi-agent architectures.
Answer along these lines: “ReAct (Reasoning + Acting) alternates between reasoning steps and action execution, making decisions observable. Planning-based agents create complete action sequences upfront, then execute—better for complex, predictable tasks. Multi-agent systems distribute tasks across specialized agents. Hybrid approaches combine patterns based on task complexity. Each pattern trades off between flexibility, interpretability, and execution efficiency.”
🚫 Avoid: Only knowing one pattern, not understanding when to use different approaches, missing the trade-offs.
If you’re looking for comprehensive resources on agentic 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 Agentic Workflows?
What to focus on: Understanding of persistence, context management, and failure recovery.
Answer along these lines: “Implement explicit state storage with versioning for workflow progress, intermediate results, and decision history. Use checkpointing at critical workflow steps to enable recovery. Maintain both short-term context (current task) and long-term memory (learned patterns). Design state to be serializable and recoverable. Include state validation to detect corruption. Consider distributed state for multi-agent systems with consistency guarantees.”
🚫 Avoid: Relying only on conversation history, not considering failure recovery, missing the need for explicit state management.
# Tool Integration and Orchestration
// 4. Design a Robust Tool Calling System for an AI Agent
What to focus on: Error handling, input validation, and scalability considerations.
Answer along these lines: “Implement tool schemas with strict input validation and type checking. Use async execution with timeouts to prevent blocking. Include retry logic with exponential backoff for transient failures. Log all tool calls and responses for debugging. Implement rate limiting and circuit breakers for external APIs. Design tool abstractions that allow easy testing and mocking. Include tool result validation to catch API changes or errors.”
🚫 Avoid: Not considering error cases, missing input validation, no scalability planning.
Watch Tool Calling Is Not Just Plumbing for AI Agents — Roy Derks to understand how to implement tool calling in your agentic applications.
// 5. How Would You Handle Tool Calling Failures and Partial Results?
What to focus on: Graceful degradation strategies and error recovery mechanisms.
Answer along these lines: “Implement tiered fallback strategies: retry with different parameters, use alternative tools, or gracefully degrade functionality. For partial results, design continuation mechanisms that can resume from intermediate states. Include human-in-the-loop escalation for critical failures. Log failure patterns to improve reliability. Use circuit breakers to avoid cascading failures. Design tool interfaces to return structured error information that agents can reason about.”
🚫 Avoid: Simple retry-only strategies, not planning for partial results, missing escalation paths.
Depending on the framework you’re using to build your application, you can refer to the specific docs. For example, How to handle tool calling errors covers handling such errors for the LangGraph framework.
// 6. Explain How You’d Build a Tool Discovery and Selection System for Agents
What to focus on: Dynamic tool management and intelligent selection strategies.
Answer along these lines: “Create a tool registry with semantic descriptions, capabilities metadata, and usage examples. Implement tool ranking based on task requirements, past success rates, and current availability. Use embedding similarity for tool discovery based on natural language descriptions. Include cost and latency considerations in selection. Design plugin architectures for dynamic tool loading. Implement tool versioning and backward compatibility.”
🚫 Avoid: Hard-coded tool lists, no selection criteria, missing dynamic discovery capabilities.
# Planning and Reasoning
// 7. Compare Different Planning Approaches for AI Agents
What to focus on: Understanding of hierarchical planning, reactive planning, and hybrid approaches.
Answer along these lines: “Hierarchical planning breaks complex goals into sub-goals, enabling better organization but requiring good decomposition strategies. Reactive planning responds to immediate conditions, offering flexibility but potentially missing optimal solutions. Monte Carlo Tree Search explores action spaces systematically but requires good evaluation functions. Hybrid approaches use high-level planning with reactive execution. Choice depends on task predictability, time constraints, and environment complexity.”
🚫 Avoid: Only knowing one approach, not considering task characteristics, missing trade-offs between planning depth and execution speed.
// 8. How Do You Implement Effective Goal Decomposition in Agent Systems?
What to focus on: Strategies for breaking down complex objectives and handling dependencies.
Answer along these lines: “Use recursive goal decomposition with clear success criteria for each sub-goal. Implement dependency tracking to manage execution order. Include goal prioritization and resource allocation. Design goals to be specific, measurable, and time-bound. Use templates for common goal patterns. Include conflict resolution for competing objectives. Implement goal revision capabilities when circumstances change.”
🚫 Avoid: Ad-hoc decomposition without structure, not handling dependencies, missing context.
# Multi-Agent Systems
// 9. Design a Multi-Agent System for Collaborative Problem-Solving
What to focus on: Communication protocols, coordination mechanisms, and conflict resolution.
Answer along these lines: “Define specialized agent roles with clear capabilities and responsibilities. Implement message passing protocols with structured communication formats. Use coordination mechanisms like task auctions or consensus algorithms. Include conflict resolution processes for competing goals or resources. Design monitoring systems to track collaboration effectiveness. Implement load balancing and failover mechanisms. Include shared memory or blackboard systems for information sharing.”
🚫 Avoid: Unclear role definitions, no coordination strategy, missing conflict resolution.
If you want to learn more about building multi-agent systems, work through Multi AI Agent Systems with crewAI by DeepLearning.AI.
# Safety and Reliability
// 10. What Safety Mechanisms Are Essential for Production Agentic AI Systems?
What to focus on: Understanding of containment, monitoring, and human oversight requirements.
Answer along these lines: “Implement action sandboxing to limit agent capabilities to approved operations. Use permission systems requiring explicit authorization for sensitive actions. Include monitoring for anomalous behavior patterns. Design kill switches for immediate agent shutdown. Implement human-in-the-loop approvals for high-risk decisions. Use action logging for audit trails. Include rollback mechanisms for reversible operations. Regular safety testing with adversarial scenarios.”
🚫 Avoid: No containment strategy, missing human oversight, not considering adversarial scenarios.
To learn more, read the Deploying agentic AI with safety and security: A playbook for technology leaders report by McKinsey.
# Wrapping Up
Agentic AI engineering demands a unique combination of AI expertise, systems thinking, and safety consciousness. These questions probe the practical knowledge needed to build autonomous systems that work reliably in production.
The best agentic AI engineers design systems with appropriate safeguards, clear observability, and graceful failure modes. They think beyond single interactions to full workflow orchestration and long-term system behavior.
Would you like us to do a sequel with more related questions on agentic AI? Let us know in the comments!
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.
