When (and How) to Build Production AI Agents
Separate deterministic workflows from genuine multi-agent autonomy, then build with Google ADK, MCP tools, and token circuit breakers.
Audience: Backend, Platform & Applied AI Engineers
Pillar 03 · Courses & Workshops
Hands-on AI engineering enablement for technical teams and leaders.
Taking engineering organizations from prompt prototypes to evaluated, production-grade agentic and ML systems.
Live Corporate Enablement
Customizable enterprise AI workshops
Corporate Training & Enablement Delivered With
LinkedIn Learning ·
Morgan Stanley ·
FedEx ·
Accenture ·
Roblox ·
Silicon Valley Security Group ·
AI Aspire ·
CommonSpirit Health ·
Udacity
Evaluating, Red-Teaming & Guardrailing LLM Systems
Build automated evaluation harnesses from scratch: trajectory grading, Pass@k metrics, LLM comparators, PII redaction, and injection defenses.
Audience: ML Engineers, Tech Leads & Quality Teams
AI Architecture & Strategy for Engineering Leaders
Evaluate build-vs-buy trade-offs, inference unit economics, RAG vs. fine-tuning ROI, and organizational readiness for agentic workflows.
Audience: CTOs, VPs of Engineering, Directors & Founders
On-Demand Curriculum · 44,000+ Learners · 4.6–4.8★ (180+ Ratings)
LinkedIn Learning & Udacity Courses
Evaluating AI Agents with Google ADK ↗
Systematic agent evaluation covering golden datasets, trajectory assertions, CI/CD batch runs, Pass@k reliability, and LLM-as-a-Judge rubrics.
Agent Evaluation
Google ADK
Pass@k & LLM-as-a-Judge
Hands-On AI: Building Agents with Google's Agent Development Toolkit (ADK) ↗
Code-first curriculum on custom tool schemas, session memory, sequential/parallel/loop multi-agent orchestration, and safety callbacks.
Google ADK
Multi-Agent Orchestration
Safety Callbacks
Integrating AI into the Product Architecture ↗
Systems blueprint for embedding foundation and classical ML models—balancing latency SLAs, caching, RAG vs. fine-tuning, and observability.
AI Systems Architecture
RAG vs. Fine-Tuning
Latency & Cost
Building AI Products: Implementing Responsible AI ↗
Engineering practices for privacy-preserving ML, bias auditing, PII redaction, red-teaming, and human-in-the-loop UX.
Responsible AI
Privacy & Red-Teaming
GenAIOps
Cloud Platform Engineering & Production AI (Enterprise Curriculum) ↗
Hands-on Google Cloud infrastructure, agentic systems, and AI-ready data pipeline labs created as a Udacity Instructor & AI Content Creator.
Google Cloud
Platform Engineering
Enterprise Enablement
Verified Learner & Research Feedback
★★★★★ 5.0★
LinkedIn Learning ↗
“Clear, practical, and actually connects the pieces in a way that makes sense.”
★★★★★ 5.0★
LinkedIn Learning ↗
“Very Good and Yes, I suggested to others”
★★★★★ 5.0★
Topmate ↗
“I appreciate your time, interest, and knowledge and acknowledge your contribution to my study. Thank you for being part of my doctoral journey!”
Enablement FAQ
Common questions
What organizations has Jigyasa delivered corporate AI training with?
She has trained 44,000+ learners on LinkedIn Learning and delivered corporate AI training and workshops with LinkedIn Learning, Morgan Stanley, FedEx, Accenture, Roblox, Silicon Valley Security Group, AI Aspire, CommonSpirit Health, and Udacity.
Can workshops be tailored to our internal stack?
Yes—sessions run as 90-minute executive briefings, half-day intensives, or full-day labs customized to Python, Google ADK / Vertex AI, or multi-provider LLM stacks.