AI Agent vs Agentic AI: Top 10 Interview Q&A (Accenture Experience)

🌟 My Gen AI Interview Experience at Accenture

Last week, I interviewed for a Generative AI Specialist role at Accenture, and the discussion turned into an in-depth conversation on “AI Agent vs Agentic AI.”

Since my resume mentions LangChain projects and building agentic workflows for an internal chatbot, the interviewer focused heavily on how well I understand autonomous agents vs rule-based AI systems.

Here’s exactly how the conversation unfolded:

📝 Top 10 Real Interview Questions and My Detailed Answers

✅ Q1: I see you’ve built a chatbot in your previous company. Was it an AI Agent or Agentic AI? How would you differentiate the two?

My Answer:
In my previous role, I designed a customer support chatbot using Rasa and later extended it with LangChain. Initially, it was a basic AI Agent:

• It followed pre-defined intents and actions.
• Could answer FAQs or create tickets but couldn’t handle anything outside its training set.

Later, we evolved it into an Agentic AI system:

• Added LangChain Toolchains for dynamic reasoning.
• Integrated a vector database (Pinecone) to store past conversations for context retention.
• Enabled it to use APIs for multi-step workflows like “check order status, update address, and escalate to human if needed.”

The key difference:

• AI Agent was reactive, responding only to specific inputs.
• Agentic AI became proactive, handling ambiguous queries and chaining tasks autonomously.

The interviewer probed: “Did you design the planning module for tool selection or use prebuilt LangChain agents?”

I explained:

• Initially, we used LangChain’s Zero-Shot Agent, but later built a custom planning module using Thought-Action-Observation loops for better control.

✅ Q2: Explain the sense-think-act loop of AI Agents with an example from your project.

My Answer:
In my chatbot’s AI Agent phase:

• Sense: It parsed user queries using NLU models.
• Think: Used an intent-classifier to match pre-set intents.
• Act: Executed pre-configured actions like fetching from a database.

Example:

• User: “What’s my order status?”
• Agent: Detected intent “get_order_status” → Called API → Replied.

No reasoning beyond predefined flows.

The interviewer challenged: “How would Agentic AI improve this flow?”

I responded:

• Agentic AI would reason:
  • “If order ID is missing, ask for it.”
  • “If API fails, attempt retries or switch to alternative APIs.”
  • “If user asks for an unrelated task, pause and clarify.”
• Added self-corrective steps using LangGraph to handle such multi-turn logic.

✅ Q3: Can you share how your resume’s Agentic AI project enabled autonomy?

My Resume Project:
“Built an Agentic AI system for HR automation using GPT-4 + LangChain, enabling dynamic resume screening and interview scheduling.”

My Answer:
This system was agentic because:

• It planned tasks dynamically: Screening resumes, ranking candidates, sending interview invites.
• Used external tools: Called APIs for calendar invites, Slack notifications, and even rejection emails.
• Implemented a reflection step: If invite failed due to time conflict, it re-planned alternative slots without human intervention.

The interviewer asked: “Did it handle ethical concerns like biased screening?”

I explained:

• Added guardrails using OpenAI moderation API to flag sensitive or biased content.

✅ Q4: Why are enterprises like Accenture moving from AI Agents to Agentic AI?

My Answer:

• Scalability: AI Agents can’t handle unpredictable scenarios; Agentic AI thrives there.
• Autonomy: Reduces need for humans in the loop.
• Complex Task Orchestration: Useful for multi-step tasks like legal document drafting, onboarding workflows, or IT troubleshooting.

I referenced a recent Gartner report that predicts “50% of enterprise AI systems will exhibit agentic capabilities by 2027.”

✅ Q5: What challenges did you face while building an Agentic AI system?

My Answer:

• Tool Chaining Failures: Sometimes APIs returned unexpected data, breaking the chain.
  • Solution: Added fallback strategies and test harnesses.
• Hallucinations in LLM reasoning:
  • Solution: Combined RAG (Retrieval Augmented Generation) with fact-check steps before executing actions.

The interviewer was impressed with this answer and said: “We need candidates who can anticipate and solve such real-world problems.”

✅ Q6: Which frameworks would you use today to build Agentic AI and why?

My Answer:

• LangGraph (by LangChain) → For multi-step workflows.
• Auto-GPT → For quick prototypes.
• CrewAI → For multi-agent collaboration.
• OpenAI’s Function Calling → To tie LLM reasoning with APIs.

✅ Q7: How does Agentic AI handle memory differently from AI Agents?

My Answer:

• AI Agents: Stateless or store only session-level context.
• Agentic AI:
  • Uses long-term memory (Vector DBs like Pinecone).
  • Maintains episodic memory across sessions.

✅ Q8: What is the role of Thought-Action-Reflection loops in Agentic AI?

My Answer:

• Thought: Plan what to do.
• Action: Execute API calls, tools, etc.
• Reflection: Analyze outcomes and decide next steps.

This loop allows self-correction and adaptive reasoning, making Agentic AI more human-like.

✅ Q9: How would you prevent an Agentic AI system from taking unintended actions?

My Answer:

• Set strict permissions for API access.
• Add human-in-the-loop checkpoints for critical decisions.
• Use Guardrails AI or OpenAI Moderation API for safety layers.

✅ Q10: If given a chance, how would you apply Agentic AI in Accenture’s enterprise ecosystem?

My Answer:

• Finance: Automate invoice reconciliation.
• HR: End-to-end onboarding workflows.
• IT: Autonomous troubleshooting bots for employee queries.

I also proposed integrating Accenture’s internal tools (like myWizard) with Agentic AI for enhanced automation.

🎯 Key Takeaways for Gen AI Candidates

✅ Back up your answers with real projects from your resume.
✅ Learn frameworks like LangChain and LangGraph.
✅ Prepare challenges + solutions to show practical expertise.

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