--AI Horizon Watchers--

Dear AI Community, 

In this Issue you read

• | 1. Build Real World AI Agents
• | 2. Legal AI Agents
• | 3. Build AI Agents from Scratch
• | 4. Manufacturing AI Agents
• | 5. New Free AI Agents Guide (56 pages)
• | 6. Types of AI Agents - Drug Discovery
• | 7. Student from Vietnam Featured
• | 8. AI Agents 50 Best Practices

Enjoy! 

Dr. Maryam Miradi 

VP & Chief AI Scientist

Profound Analytics 

Want to Build Real-World AI Agents?

Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟱-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴,

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2,300+ (⭐⭐⭐⭐⭐) builders worldwide!
 

Just Added

➠ 11 Real-World Projects with Full Code

➠  Advanced Topics like Context Engineering

➠  Enterprise Projects Like Supply Chain
➠  China & Taiwan added to Countries

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Investment: $549

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Youtube Video

Complex PDF Parsers

Most developers build "happy path" agents. Watch me build a 5-step production-ready legal AI agent system using Google ADK, MCP standardization, and Docling and Gemini Pro 3 as LLM.

Perfect for developers, AI engineers, MLOps engineers, and AI practitioners building real-world AI Agents.

I Build in This Video: Understands Business Constraints (PydanticAI) Maps Data Reliability (Uncertainty Profiling) Parses Complex Legal Docs (Benchmarking Docling vs. LlamaIndex vs. PyPDF) Engineers the Flow (Google ADK Router & Auditor Agents) Standardizes for Reusability (MCP Server)

👨‍💻 Tech Stack Used: Google Agent Development Kit (ADK) Google Antigravity Google Gemini Pro Docling (IBM) Model Context Protocol (MCP) PydanticAI Python

💡 Key Concepts Covered:

✅ Fail-safe defaults that prevent naive agent behavior

✅ Input validation for regulatory compliance

✅ OCR confidence checking and document quality gates

✅ Human-in-the-loop escalation mechanisms

✅ Data surface mapping for uncertainty profiling

✅ Multi-agent routing with regeneration logic

✅ MCP server architecture for cross-project reuse

Full breakdown:
👉 youtu.be/LegalAIAgents

Roadmaps

Build AI Agents from Scratch

🛠️🧭 How to Build AI Agents from Scratch – Even If You’ve Never Done It Before.
𝗧𝗵𝗶𝘀 𝗶𝘀 𝟭𝟬 𝗦𝘁𝗲𝗽 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗿𝗼𝗺 𝗽𝗿𝗼𝗺𝗽𝘁 𝘁𝗼 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻.

》𝗦𝘁𝗲𝗽 𝟭: Define the Agent’s Role and Goal

》𝗦𝘁𝗲𝗽 𝟮: Design Structured Input & Output

》𝗦𝘁𝗲𝗽 𝟯: Prompt, Tune, and Define Agent Protocol

》𝗦𝘁𝗲𝗽 𝟰: Add Reasoning and Tool Use

》𝗦𝘁𝗲𝗽 𝟱: Structure Multi-Agent Logic (if needed)

》𝗦𝘁𝗲𝗽 𝟲: Add Memory and Long-Term Context

》𝗦𝘁𝗲𝗽 𝟳: Add Voice or Vision Capabilities (Optional)

》𝗦𝘁𝗲𝗽 𝟴: Deliver the Output (in Human or Machine Format)

》𝗦𝘁𝗲𝗽 𝟵: Wrap in a UI or API (Optional)

》𝗦𝘁𝗲𝗽 𝟭𝟬: Evaluate and Monitor Your Agent’s Performance 

 Read more here:

linkedin.com/maryamiradi/aiagentsroadmap

AI Agents Application

Manufacturing

Manufacturing demands precision yet drowns in complexity. 

AI agents with RAG and knowledge graphs crack its toughest challenges.

♕ Challenges in Manufacturing:
⭒ Need for Customization
⭒ Need for Shorter product life cycles
⭒ Intense global competition

Robotics and classic machine learning are constrained.

♘ Some usecases:
⭒ Production Scheduling
⭒ Process Optimization
⭒ Supply Chain Resilience

 ♙ Transformation:
⭒ From Task Execution to Goal-Driven Optimization
⭒ From Rule-Based Control to Adaptive Planning
⭒ From Localized Optimization to System-Level Orchestration
⭒ From Static Execution to Continuous Learning and Evolution

♖ Methods:
⭒ Knowledge-Enhanced Semantic Retrieval and Automated Documentation
⭒ Multimodal Cognitive Perception and Contextual Reasoning
⭒ Adaptive Learning and Evolutionary Optimization

♗ Future Challenges to tackle:
⭒ Cross-Format Document Parsing in Manufacturing
⭒ Multimodal Knowledge Extraction and Alignment in Manufacturing
⭒ Interpretability and Explainability in Manufacturing
⭒ Workforce and Organizational Resistance
⭒ Accountability and ROI Concerns

♧ AI agents empower smart manufacturing by knowledge integration, real-time decision-making, and multimodal perception, giving manufacturing efficiency, flexibility, and adaptability.

Read paper here

NEW Free Guide

AI Agents 50 Battle-tested Tips

What I Learned After Building 400+ AI Agents 

Steal the 50 Most Practical Golden Tips inside.

————————————-
𝟱𝟲-𝗣𝗮𝗴𝗲 Guide | Field-Tested | Free
𝗚𝗿𝗮𝗯 𝘁𝗵𝗲 𝗚𝘂𝗶𝗱𝗲 𝗛𝗲𝗿𝗲: 👇

maryammiradi.com/free-aiagents-guide

Real-World AI Agents 

6 Type of AI Agents 

6 AI Agent Types Every AI Engineer Should Know in 2026:

(Drug Discovery Multi-Agent Edition)

1. TYPE / ROLE: Reasoner Agent (Planner)

DESCRIPTION: Interprets results from previous iterations and decides the next action using structured reasoning and constraints, not free-form generation.
USE CASES: Explore vs exploit decisions, selecting molecule families to expand, choosing which objectives to optimize next.

2. TYPE / ROLE: Generator Agent (Candidate Builder)
DESCRIPTION: Generates large batches of molecule candidates conditioned on the target protein pocket. Optimized for coverage and speed rather than precision.
USE CASES: Rapid molecule ideation, pocket-aware generation, expanding the chemical search space beyond local optima.

3. TYPE / ROLE: Evaluator Agent (Quality Gate)
DESCRIPTION: Scores and filters each molecule using strict pharmaceutical rules and thresholds. Functions as an automated test suite for candidates.
USE CASES: Drug-likeness validation, binding affinity checks, synthetic accessibility filtering, Lipinski compliance, novelty screening.

4. TYPE / ROLE: Optimizer Agent (Iterative Improver)
DESCRIPTION: Refines promising molecules through controlled structural edits while preserving or improving binding. Operates in iterative improvement loops.
USE CASES: Multi-objective optimization, improving QED without sacrificing affinity, reducing synthesis complexity, repairing near-miss candidates.

5. TYPE / ROLE: Screener Agent (Diversity Curator)
DESCRIPTION: Clusters candidates and selects a diverse subset to avoid over-concentration in a single chemical family.
USE CASES: Fingerprint-based clustering, top-k selection per cluster, diversity enforcement for downstream testing.

6. TYPE / ROLE: Memory Agent (Experiment Ledger)
DESCRIPTION: Persistently records molecules, scores, actions, and outcomes to inform future reasoning and enable long-horizon learning.
USE CASES: Learning across iterations, preventing redundant exploration, identifying successful strategies, compounding system performance.

Read paper here: 

arxiv.org/pdf/DrugDiscoverypaper

Featured:

I awarded one of my students both a Certificate of Completion and a Certificate of Excellence.

Tran didn’t just watch the lessons. He ran the code, broke things, asked precise questions, and improved every iteration.

My 2,300 students Build Real-Worl AI Agents in 90+ countries.

Ready to join them?

➡️ maryammiradi.com/ai-agents-mastery

AI Agents

50 Best Practices

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AI pros are loving this stunning book.

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