Track 3: Enterprise RAG, Vector Search & Agentic Workflows⏱️ 45 minPRO MODULE
Agentic Tool Calling, Guardrails & LLM Evaluation (Ragas)
Build autonomous multi-step agents that call APIs reliably, handle schema validation failures, and run automated evaluation suites.
🎯 What You Will Be Able to Do
- •Implement deterministic function calling using strict JSON schemas.
- •Apply ReAct and Plan-and-Solve agent loops with loop-termination guardrails.
- •Run Ragas automated evaluation metrics: Faithfulness, Answer Relevance, Context Precision.
📋 Knowledge Required Beforehand
- •Enterprise RAG Architecture
SECTION 1 • ARCHITECTURE CONCEPTS & SYSTEM PATTERNS
Free to ReadMaking Agents Safe for Enterprise Deployments
Enterprises do not want non-deterministic agents running wild on production databases. An enterprise agent framework requires:
- 1Strict Output Typing: Using Pydantic / Zod / Gemini Schema validation to guarantee JSON structure.
- 2Deterministic Guardrails: Maximum step count (N \le 5), budget caps, and confirmation gates for write actions (e.g.,
execute_wire_transferrequires human approval). - 3Continuous Metric Evaluation (Ragas):
- •Faithfulness: Are claims in the answer grounded only in retrieved context? (Hallucination detection).
- •Answer Relevance: Did the agent actually address the prompt?
- •Context Recall: Did retrieval bring back all facts necessary to answer?
SECTION 2 • KNOWLEDGE CHECKPOINT & OBJECTIVE ASSESSMENT
📝Knowledge Checkpoint • 3 Questions
Knowledge Checkpoint: Agentic Workflows & LLM Evaluation
Passing threshold: 70%
Q1Single Choice
30 ptsIn a ReAct (Reasoning + Acting) autonomous agent loop, what safety mechanism prevents the model from entering an infinite tool-calling loop?
Q2Multiple Select
35 ptsIn the Ragas evaluation framework for RAG applications, which metric measures whether all claims in the generated response are grounded in the retrieved context? (Select all that apply)
Q3Architectural Explanation
35 ptsAn agent has access to a SQL execution tool on an enterprise database. What guardrails must an FDE enforce before executing agent-generated queries?
Formulate your technical explanation, then verify against the evaluation criteria below:
Self-Evaluation Quality Checklist:
SECTION 3 • ENTERPRISE HANDS-ON PROJECT LAB
🛠️Hands-on Project Lab • Advanced • ~50m
Lab 6: ReAct Agent Loop with Guardrails & Evaluation
Specification & Code Workspace (python):
Auto-saved to your browser session • Secure offline workspace
Lab Acceptance Criteria & Deliverables Checklist:0 of 3 verified
Work is saved automatically • Switch tabs to inspect hints or deliverables
🎙️
Interactive PracticeRelated FDE Interview Questions & Evaluation Expectations
How do you systematically prove to a Chief Risk Officer that your RAG application will not hallucinate confidential answers?
HardWhat Interviewers Expect:
•Introduce an automated synthetic evaluation pipeline (e.g. Ragas / TruLens) measuring Faithfulness score across thousands of historical queries.
•Enforce strict document-level Access Control Lists (ACLs) checked at the database/index level before retrieval, not via prompt engineering.
•Implement a Secondary Fact-Checking Guardrail: A small, deterministic model compares the generated answer strictly against retrieved source citations and masks ungrounded statements.
Recommended Reference Reading
Agentic Tool Calling, Guardrails & LLM Evaluation (Ragas) Progression & Milestones
0 of 3 Stages Cleared1. Architecture Concepts○
Core operating mechanics & patterns
2. Knowledge Checkpoint○
Objective questions & rubric evaluation
3. Hands-on Project Lab○
Enterprise scenario implementation
Progress is preserved and synced across your devices.
RECOMMENDED NEXT STEP
Next Module: Enterprise Containerization: Multi-Stage Builds & Kubernetes Security
Continue your progression in Track 3: Enterprise RAG, Vector Search & Agentic Workflows.