The 90-Day Vertical AI Agent Development Bootcamp: Hands-On VAIA Implementation

Course Details

Why This Course?

Most AI courses teach you how to call OpenAI’s API and call it a day. This bootcamp is different. You’ll build a production-grade Suspicious Activity Report (SAR) Generation Agent—the kind of AI system that needs to explain every decision it makes, handle sensitive financial data, and operate under regulatory microscopes.

Think about it: when JPMorgan Chase or HSBC deploy AI for compliance, they can’t just trust a black box. A single missed SAR costs millions in fines. A false positive wastes investigator time. Your agent needs to be explainable, auditable, and deterministic while still being intelligent.

This isn’t about chatbots that tell jokes. This is about building AI systems where mistakes have consequences, and you’ll learn to handle that pressure through code.

What You’ll Build

By day 60, you’ll have shipped a complete Financial Compliance Vertical AI Agent with:

• Multi-stage reasoning pipeline that breaks SAR generation into traceable steps (transaction analysis → pattern detection → regulatory mapping → report drafting)

• Audit trail infrastructure capturing every LLM call, decision point, and data transformation with cryptographic verification

• Deterministic evaluation suite with 200+ test cases covering edge cases real compliance teams encounter

• Human-in-the-loop workflow where agents draft SARs but compliance officers retain final authority

• Fallback mechanisms for handling ambiguous cases, missing data, and regulatory gray areas

• Performance monitoring tracking latency, token costs, accuracy metrics, and drift detection

• Compliance controls for PII handling, data retention, access logging, and regulatory reporting

Who Should Take This Course?

You’re a perfect fit if you:

• Have 1+ years writing production code in Python

• Understand REST APIs, databases, and cloud basics (you’ve deployed something before)

• Want to move beyond “prompt engineering” into building reliable AI systems

• Need to understand AI compliance for fintech, healthcare, or regulated industries

• Want to architect systems where AI failure modes are designed, not discovered

You’re not ready if you:

• Haven’t written a web service or worked with APIs before

• Expect no-code solutions or drag-and-drop tools

• Think AI is magic that “just works” without engineering discipline

This bootcamp assumes you can debug a stack trace and read AWS documentation. We’ll teach you the AI-specific patterns, but you need software fundamentals first.

What Makes This Course Different?

1. Compliance-First Architecture
Most AI courses ignore the hardest part: making systems auditable. You’ll build logging, versioning, and explainability into every component from day one—because that’s how regulated systems actually work.

2. Cost Engineering Throughout
GPT-4 calls cost real money. You’ll instrument token usage, implement caching strategies, and learn when to use smaller models. By week 6, you’ll optimize your agent’s monthly cost from $2,000 to $200 without sacrificing quality.

3. Failure Mode Design
We dedicate entire lessons to “what happens when the LLM hallucinates?” and “how do we handle API timeouts?” You’ll build circuit breakers, retry logic, and graceful degradation—the unglamorous code that separates MVPs from production systems.

4. Real Regulatory Requirements
Our SAR agent follows actual FinCEN guidelines (BSA/AML compliance). You’ll learn to map regulatory text to system behavior, not just follow vague “best practices.”

5. No Fluff, Just Code
Every lesson is 90 minutes of implementation. You write code, run tests, deploy changes. No slideware, no theory lectures, no “imagine if we could...”

Key Topics Covered

Vertical AI Fundamentals

• Why vertical agents outperform general-purpose chatbots in specialized domains

• Deterministic vs. probabilistic decision points in compliance workflows

• Designing agent behavior specifications that satisfy regulators

LLM Engineering Patterns

• Structured output extraction with JSON schemas and Pydantic validation

• Chain-of-thought prompting for transparent reasoning trails

• Few-shot learning with compliance-specific examples

• Prompt versioning and A/B testing infrastructure

Compliance & Auditability

• Cryptographic audit logs using Merkle trees for tamper-proof records

• PII redaction and tokenization for GDPR/CCPA compliance

• Drift detection monitoring for model behavior changes

• Regulatory report generation for examiner requests

Production Operations

• Event-driven architecture with AWS Step Functions for long-running workflows

• Cost optimization through prompt compression and model selection

• Error budgets and SLA design for AI systems

• Blue-green deployments for agent updates without downtime

Testing & Evaluation

• Deterministic test suites that don’t rely on “vibes”

• Shadow mode deployment for validating agent changes

• Human evaluation frameworks for compliance quality

• Red-teaming exercises to discover failure modes

System Scalability

• Asynchronous processing for 10,000 transactions/day

• Batch inference optimization to reduce API costs

• Caching strategies for repeated analysis patterns

• Rate limiting and quotas to prevent runaway costs

Prerequisites

Required Skills:

• Python 3.10+: You should comfortably write classes, handle exceptions, and use type hints

• API Development: Built or consumed REST APIs; understand HTTP, JSON, authentication

• Cloud Basics: Deployed something to AWS/GCP/Azure; familiar with serverless concepts

• Version Control: Daily Git user; can resolve merge conflicts and manage branches

• SQL Fundamentals: Write SELECT queries, understand indexes, know when to use databases vs. object storage

Required Tools:

• AWS Account (free tier sufficient for first 30 days; ~$50/month thereafter)

• OpenAI API key (~$100 budget for 60 days of development)

• Local development environment: Python 3.10+, Docker, VS Code or PyCharm

• GitHub account for code versioning and portfolio showcase

Helpful But Not Required:

• Experience with financial systems or compliance workflows

• Familiarity with LangChain, Anthropic Claude, or other LLM frameworks

• Infrastructure-as-code experience (Terraform/CloudFormation)

• Understanding of regulatory standards (BSA, GDPR, SOC2)

Course Structure

Daily Commitment: 90 minutes of focused coding (60 min implementation + 30 min testing/debugging)

Weekly Rhythm:

• Days 1-5: Build new features, implement core logic

• Day 6: Integration day—connect the week’s components into working pipeline

• Day 7: Review, refactor, document—solidify understanding before moving forward

Four Major Phases:

Phase 1: Foundation (Days 1-15)
Build the MVP agent that can generate basic SARs. Focus on core workflows, data models, and LLM integration. By day 15, you’ll have a working prototype processing simple transaction patterns.

Phase 2: Compliance Infrastructure (Days 16-30)
Add audit logging, explainability features, and regulatory controls. Transform your MVP into something that could pass a compliance review. You’ll implement the “boring” infrastructure that makes AI trustworthy.

Phase 3: Production Readiness (Days 31-45)
Scale your agent with proper error handling, monitoring, cost optimization, and performance tuning. Deploy to AWS with CI/CD pipelines. Learn to operate AI systems, not just build them.

Phase 4: Advanced Capabilities (Days 46-60)
Add sophisticated features: multi-agent collaboration, real-time transaction monitoring, adaptive learning from human feedback, and advanced evaluation frameworks. Ship a system you’d be proud to show in a technical interview.

Deliverables:

• GitHub repository with 60 commits (one per day) showing your progression

• Deployed SAR Generation Agent on AWS with monitoring dashboards

• Technical documentation covering architecture decisions and compliance controls

• Portfolio case study explaining design choices and trade-offs

• 200+ test cases demonstrating system reliability