Phoenix Softnet Technologies - India

Senior Software Engineer

Ocean IT Solutions

Full Stack Developer

𝐀𝐈 𝐄𝐧𝐭𝐡𝐮𝐬𝐢𝐚𝐬𝐭 | 𝐅𝐮𝐥𝐥 𝐒𝐭𝐚𝐜𝐤 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 | 𝐆𝐫𝐨𝐰𝐭𝐡 𝐌𝐚𝐫𝐤𝐞𝐭𝐞𝐫 | 𝐒𝐡𝐚𝐫𝐢𝐧𝐠 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐨𝐧 𝐀𝐈 | 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐖𝐫𝐢𝐭𝐞𝐫

As a software developer with 3 years of experience, I am passionate about crafting high-quality software that solves complex problems and delivers value to end-users. With a strong background in Flutter,ASP .NET and Database technologies, I thrive on collaborating with cross-functional teams to design and implement solutions that meet business requirements.

My expertise spans the entire software development lifecycle, from requirements gathering and architecture design to coding, testing, deployment, and maintenance. I am comfortable working with a wide range of programming languages, frameworks, and tools, and am always eager to learn new technologies to expand my skill set.

If you are looking for a software developer who can help you achieve your business goals, please don't hesitate to get in touch. I am excited to explore new opportunities and to leverage my skills and experience to deliver value to your organization.

Aakash K.

Wells Fargo

While my formal title is Executive Director (a banking band level), my work is hands-on architecture and engineering:

Enterprise Architecture – designing scalable AI and data platforms across the bank’s global footprint.

Generative AI & Agentic Systems – applying advanced LLMs, multi-agent orchestration, and AI governance frameworks to real-world financial use cases.

Cloud & Data Engineering – building secure, resilient, and high-performance architectures leveraging GCP and modern data stacks.

Architecture Leadership – collaborating with product, engineering, and business teams to align enterprise strategy with technical execution.

Principal Architect - Generative AI ( Executive Director)

↳ Designed a Hybrid RAG system combining vector search and keyword-boosted retrieval, increasing knowledge discovery accuracy by over 60%

↳ Led prompt engineering, Fine-tuning, and backend integration for production-grade GenAI services

↳ Optimized Python-based data processing pipelines, boosting throughput by 45% and enabling real-time analytics

↳ Reduced infrastructure costs and system downtime through cloud-native engineering using AWS Lambda, Fargate, and SQS

↳ Embedded explainability, bias detection, and privacy-aware design into AI services to ensure responsible AI practices

↳ Built end-to-end MLOps pipelines and CICD workflows to enable rapid iteration and secure deployment

↳ Hands-on coding, writing infrastructure-as-code, and mentoring engineering teams on LLMOps and agentic system design

↳ Collaborated across cross-functional teams to align AI innovation with enterprise goals and operational reliability

Principal Engineer

Packt

Authored the groundbreaking technical book "Building ETL Pipelines with Python," filling a critical gap in the data engineering literature.

- Authored pioneering technical book on production-grade ETL pipelines using Python, addressing a critical gap in data engineering literature
- Focused on scalable architectures for petabyte-level data processing, bridging academic theory and industry practices
- Provided comprehensive, hands-on guide with advanced ETL topics, real-world applications, and practical case studies
- Empowered data professionals with actionable insights for enterprise-level data infrastructures
- Established new benchmark in practical, industry-oriented literature for Python-based ETL solutions

Technical Author

Led complex software development projects, focusing on cloud-based solutions and microservices architecture.

Key Achievements:
- Spearheaded the migration of monolithic applications to microservice-based architecture using AWS, Python, Go, and Flask, significantly improving system scalability and maintainability
- Developed and implemented production-grade Proof of Concepts (POCs) for innovative solutions
- Designed and built high-performance, scalable RESTful APIs using Python (Flask) and AWS API Gateway, enhancing enterprise application capabilities
- Orchestrated efficient data pipelines leveraging AirFlow, Dask, Pandas, and Numpy for large-scale data processing

Data Engineer

Pioneered data processing and ETL solutions, focusing on big data technologies and cloud-based data services.

Key Achievements:
- Architected and implemented a sophisticated data pipeline using Airflow, Kafka, and Python, capable of processing billions of records with limited computational resources
- Developed a persistent Data as a Service (DaaS) system, ensuring 24/7 data availability for users and critical business operations
- Optimized data processing workflows, significantly reducing processing time and resource utilization

Technical Contributions:
- Leveraged advanced technologies including PySpark, Kafka, and Elasticsearch for efficient big data processing
- Implemented microservices architecture using Go, RPC, AMQP, and Nameko, enhancing system modularity and scalability
- Utilized containerization and orchestration technologies like Docker and Kubernetes for streamlined deployment and management
- Employed cloud technologies including AWS Lambda, ECS, Elasticache, DynamoDB, and API Gateway to build robust, scalable data solutions

Python Data Engineer at Cigna

Cognizant

Key Contributions:
- Architected and developed a distributed, robust Crew Management System using Python, Django, and RabbitMQ
- Implemented real-time data processing and visualization, enhancing decision-making capabilities for administrators
- Designed efficient data pipelines and analytics workflows using Pandas, Numpy, and Matplotlib
- Optimized database operations through complex SQL queries and stored procedures
- Developed full-stack solutions, integrating Python backend with JavaScript frontend

Full Stack Engineer (Python) at Alaska Airlines

- Led development of IVoRI (Independent Valuation Reporting Infrastructure), a strategic reporting application, using Python, WxPython, and Flask
- Engineered efficient data pipelines and migrated complex ETL processes from Informatica to Python (Pandas), significantly improving system performance
- Implemented Test-Driven Development (TDD) practices and enhanced unit test coverage, elevating overall code quality
- Collaborated with cross-functional teams and clients to deliver mission-critical financial applications, troubleshoot issues, and implement enhancements
- Technologies: Python, WxPython, Flask, Pandas, SQL (Sybase), NoSQL, Groovy, JavaScript, Shell Scripting

Senior Python Developer at JPMorgan Chase & Co.

- Led development of the Amex Personalization Utility, a strategic project transforming product and service marketing to clients, using Java and Python
- Engineered automated solutions for data-related tasks and content management, saving 80 man-hours per week and streamlining seasonal card offer updates
- Developed Employee Roster Management system for 3M Company as a full-stack developer, utilizing Java, JavaScript, Spring, HTML, and CSS
- Implemented code coverage tasks and monitored thousands of lines of code to ensure robust software operations
- Cultivated strong client relationships, maintaining open communication channels for frequent interaction and progressive status updates

Backend Engineer at Amex

All views are strictly my own. I do not speak on behalf of my employer. I focus on publicly available knowledge, frameworks, and trends that help advance the developer and AI community.

I am an AI Architect and Principal Engineer with 15+ years of experience building scalable, intelligent systems using Python, Go, Cloud platforms, Databricks, and AI.

At ADP, I lead the design and development of cloud-native, microservices-based applications and AI-driven platforms. I architect end-to-end solutions combining ETL pipelines, LLMs, and real-time analytics, collaborating cross-functionally with product, SRE, and security teams to deliver business-critical innovations.

As an AI strategist, author, and technology mentor, I specialize in applying Large Language Models, MLOps, and modern AI architectures to solve real-world problems. I’m passionate about creating value through intelligent automation, empowering teams, and driving enterprise transformation with AI.

Core Skills & Expertise

AI & ML
Generative AI, LLMs, LangChain, LangGraph, LlamaIndex, MLOps, AI-driven Analytics, MLflow, TensorFlow

Cloud & Platforms
AWS (Lambda, ECS, ElastiCache, DynamoDB, API Gateway), Azure, GCP, Databricks

Programming & Frameworks
Python, Go, JavaScript | Django, Flask, FastAPI

Data Engineering
Airflow, Spark, Pandas, NumPy | ETL/ELT Pipeline Architecture

Databases
RDBMS, NoSQL, GraphDB, Vector DBs

DevOps & Architecture
Microservices, Docker, CI/CD, API-first Architecture

Leadership
AI Strategy, Team Mentorship, Cross-functional Collaboration, Technical Roadmapping

Tools
GitLab, Bitbucket, Jira, Copilot, Postman, Splunk

Brij kishore Pandey

If you’re still feeding your LLMs with plain JSON, you’re leaving tokens, speed and budget on the table.

TOON launched last week — and it’s a game changer for anyone building with LLMs

TOON (Token-Oriented Object Notation) is the serialization format built for the era of large context, agentic workflows and multi-tool LLM systems. 

Here’s what TOON delivers:

Token efficiency: Up to 30-60% fewer tokens than formatted JSON for uniform arrays. 

LLM-friendly structure: Explicit lengths and field sets boost parsing and retrieval accuracy. 

Minimal syntax: No excess braces, quotes or repeated keys — cleaner input for faster comprehension. 

Seamless JSON compatibility: Spend in JSON, push to TOON for LLMs, convert back when needed. 

❓ When to use TOON

Ideal for large datasets with uniform object arrays (e.g., telemetry logs, user lists, product catalogs)

Less optimal for deeply nested, non-uniform structures — JSON might still win there. 

If you build agents, copilots, or any LLM system that sends structured data into a prompt — seriously, make TOON your new standard. 

Your token budget, model throughput and reliability will thank you.

AI Architect | AI Engineer | Generative AI | Agentic AI

Most people start building AI projects by writing . py files.

But sustainable, production-ready generative AI isn’t built on code alone.
It’s built on structure.

The biggest reason AI projects struggle isn’t lack of model accuracy —it’s poor architecture, organization, and maintainability.

So I built a structured Generative AI Project Template →
 designed for scalability, modularity, and real-world production use.

This isn’t just a folder layout.

It’s a blueprint for building AI systems that don’t collapse as they grow.

Github Link in comment -

RAG is no longer a “technique.”

It’s a whole ecosystem — and a developer’s stack.

Over the past 18 months, Retrieval-Augmented Generation (RAG) has quietly evolved from a simple “search + LLM” pattern into a complete engineering discipline.

Today, building AI systems isn’t just about choosing a model.

It’s about architecting the entire pipeline — from extraction to embeddings, from vector databases to evaluation, from closed models to open-source innovators.

Let me tell you about two students: Arjun and Meera.

Both are smart.

Both want to ace their exams.

But they have completely different strategies.

Arjun: The Retriever (RAG)

Arjun’s method?

He builds a giant notebook —

filled with summaries, notes, and past lessons.

Whenever he needs to answer something:

 -He quickly flips through his pages
 -Finds relevant notes
 -Connects the dots
 -And gives his answer

Efficient. Organized. Reliable — as long as his notes are updated.
But sometimes…

If his notes didn't cover a new topic,

he has to go back, learn, and re-index them
before answering confidently.

That’s RAG: Retrieval-Augmented Generation
 → fetch relevant context → answer accurately.

Meera: The Context Thinker (CAG)

Meera prepares differently.

She doesn’t just store notes —
she builds a deep understanding of context.
She learns how subjects relate,
 keeps frameworks in mind, and knows how to adapt knowledge.

When a new question comes, she:

- Understands the situation
- Injects domain context
- Merges it with prior understanding
- Cross-checks consistency
- Enriches her response with nuance

Her answers feel more natural, human-like, situationally aware.

That’s CAG: Context-Augmented Generation→ infuse domain intelligence → synchronize context → generate richer responses.

The Difference in One Line

RAG retrieves facts.

CAG understands situations.

RAG is your library.

CAG is your commonsense + domain intuition engine.

Both have strengths.

Winners combine them.

RAG built the bridge.
 CAG will accelerate the future.

 The Future Stack

 --RAG for truth
 --CAG for intelligence
 --Memory for continuity
 --Agents for action
 --Feedback loops for improvement

AI is growing not just smarter — but contextually aware.

Picture Credit - Rahul Agarwal

AI Architect | Strategist | Generative AI | Agentic AI

Most Generative AI projects don’t fail because of weak models — they fail because the engineering foundation is weak.

Teams often begin with prompts, APIs, and notebooks.

But when it’s time to scale, integrate, audit, or productionize, everything starts to break.

What really decides if your AI project moves from demo to product?

How well it is structured from day one.

The visual you see is a structured Generative AI project layout.

It covers modularity, maintainability, configuration management, prompt engineering, token usage, and logging.

It helps solve common challenges like:
 • Unstructured notebooks and prototype chaos
 • Hardcoded prompts and model settings
 • No token tracking, caching, or rate limiting
 • Mixing prompt logic with core application code
 • Difficulty moving from proof-of-concept to production

What this structure includes effectively:
 • YAML-based configurations for prompts, models, and logging
 • Modular LLM clients (GPT, Claude, Base)
 • Prompt engineering utilities: templates, chaining, few-shot design
 • Token counting, caching, logging, and rate limiters
 • Separate example scripts and experimental notebooks
 • Clear separation between utilities, handlers, and core logic

What is not included — but should be added in any production-ready GenAI system:

Security and Governance
 • Secret management (Vault, AWS Secrets Manager)
 • PII handling and compliance safeguards
 • Guardrails, content moderation, responsible use

Observability and Monitoring
 • Token usage analytics and latency tracking
 • Failure metrics, tool-call history, retry insights
 • Integration with LangSmith, Honeycomb, or Arize

Testing and Evaluation
 • Prompt regression testing
 • Response quality evaluation frameworks (RAGAS, TruLens)
 • Model behaviour tracking across iterations

Agent, Memory, and Context Management
 • Memory layers (short-term, long-term, vector-based)
 • Agent orchestration with structured task flows
 • Document retrieval, tool routing, function handlers

Ops and Deployment
 • CI/CD and container orchestration
 • Scalable API deployment (FastAPI, Lambda, Kubernetes)
 • API gateway, authentication, throttling, access control

This framework is a great start.

But scaling GenAI is less about prompts—and more about disciplined engineering.

Structure is not just documentation.

In AI systems, structure itself is a feature.

The complete GitHub template code for this structure is available in the comments.

Everyone has spent the last two years mastering the "Generative AI" layer of this universe—crafting prompts and generating content. 

But as this incredible visualization shows, that’s just the middle ring.

The real frontier is happening right now in the outer layers: AI Agents and Agentic AI.

We are shifting from models that simply respond to us, to systems capable of: 

✅ Autonomous planning and goal decomposition. 
✅ Long-term memory and context. 
✅ Multi-agent collaboration. 
✅ Tool use and function calling to execute tasks in the real world.

The "AI Universe" isn't static; it’s expanding rapidly. 

The foundation of Machine Learning and Deep Learning laid the groundwork, but the future belongs to systems that can do, not just say.

Where do you see the biggest impact for Agentic AI in your industry over the next year?

The biggest gap in Generative AI right now isn't models; it's the bridge to production.

Everyone is excited about Foundation Models (Step 3) and the dream of autonomous AI Agents (Step 6). 

But if you look closely at this updated roadmap, the real magic for businesses happens in the messy middle.

I specifically updated this visual to include Section 5a: Fine-Tuning & RAG Patterns, and added Eval/LLMOps to the stack in Section 4. 

Why?
Because dumping a raw foundation model into an application rarely works.

An AI Engineer's job today is less about training massive models from scratch and more about:

Grounding them: Using Retrieval-Augmented Generation (RAG) to connect models to your private data.

Adapting them: Using Parameter-Efficient Fine-Tuning (PEFT/LoRA) to specialize them cheaply.

Evaluating them: Moving beyond "vibes-based checking" to rigorous 
evaluation frameworks (like Ragas or TruLens) before deployment.

Don't just look at the start and end of this roadmap. Master the bridge in the middle.

Where are you currently focusing your upskilling efforts?

Free API Courses - High Quality 



I created these API guides in 2022.

 Updated it for three years.

 Shared it freely — because APIs are not just a technical skill.

They are the foundational language of intelligence systems.

If you ever want to build AI agents, orchestrators, or Agentic systems —APIs are where it truly begins.

So here is my small gift to this community — one last time.

A 12-day, focused API learning journey with carefully curated free resources:

Day 1: API Fundamentals Simplified
 ↳ lnkd.in/e8eMet_k
 ↳ lnkd.in/er9JiGxw
Day 2: API Methods & Terminologies
 ↳ lnkd.in/ey9v7-hU
 ↳ lnkd.in/eRsPMzpd
Day 3: API Authentication & Status Codes
 ↳ lnkd.in/eNPfpAdE
 ↳ lnkd.in/egXizUrS
Day 4: REST vs GraphQL + Integration Basics
 ↳ lnkd.in/eZHREdgC
 ↳ lnkd.in/eDASPP5m
Day 5: Deep Dive into API Integration
 ↳ lnkd.in/eZwFVrH7
 ↳ lnkd.in/e4WGDffA
Day 6: API Testing Fundamentals
 ↳ lnkd.in/emgmWJqH
 ↳ lnkd.in/eCPnGTGi
Day 7: Working with APIs in Python
 ↳ lnkd.in/eM23ah2y
Day 8: API Scaling Essentials
 ↳ lnkd.in/e3mZSvmn
Day 9: Developing Robust APIs
 ↳ lnkd.in/eBXzbFyg
Day 10: Hands-on with Postman
 ↳ lnkd.in/ezue3d4B
Day 11: API Security Deep Dive
 ↳ lnkd.in/e79ZYfPa
Day 12: Final Project – Build your own API solution
 ↳ Combine everything into a real API project.


If you're just starting — or guiding someone who is — use it while it's still here.

Share it with someone who needs their first win.

Their first moment of clarity.
Their first bit of confidence.

Because sometimes, the right roadmap doesn’t just teach.

It restores belief.
And that is worth giving — especially today.

We all know the basic RAG recipe: 

Chunk ➔ Embed ➔ Retrieve ➔ Generate. 

But when you move from a weekend prototype to an enterprise solution, that simple linear path breaks down.

I mapped out the architecture for building a truly Robust RAG System. 

To handle complex queries and ensure accuracy, we need to orchestrate several advanced components:

🔹 Query Construction: It’s not just about vector search. We need to leverage Graph DBs for relationships and SQL for structured data. 

🔹 Intelligent Routing: Deciding before retrieval whether to take a semantic route or a logical one. 

🔹 Advanced Indexing: Utilizing techniques like RAPTOR (recursive abstractive processing) and ColBERT for better semantic understanding. 

🔹 The Evaluation Loop: You can't improve what you don't measure. Integrating frameworks like Ragas or DeepEval is non-negotiable.

RAG isn't just a feature anymore; it's a complex system that requires engineering rigor.

Which part of this pipeline are you finding the most challenging to optimize right now?

The first wave of Generative AI was about individual productivity—generating images, drafting emails, summarizing text.

The next wave, leading into 2026, is about systemic integration.

Looking at this roadmap of 12 essential skills, the trend is undeniable. 

We are graduating from standalone tools to interconnected ecosystems.

The most critical emerging skills on this list aren't just about generating content; they are about governance and communication between systems:

Model Context Protocol (MCP)
Agent Communication Protocols
LLM Management

In the near future, your competitive advantage won't be how well you write a prompt. 

It will be how effectively you can orchestrate a team of AI agents to autonomously execute complex workflows across your business data.

The future is agentic, multimodal, and connected.

The "Prompt Engineering" era is already evolving. 

2026 belongs to the architects of autonomous systems. 

If 2024 and 2025 were about learning to talk to AI, 2026 will be about learning to build with AI. 

The baseline for competence is shifting rapidly.

We are moving beyond simple Q&A bots towards agentic workflows that can plan, reason, execute, and govern themselves.

I’ve put together this roadmap of the 15 AI Skills to Master for 2026. 

This isn't just about knowing the newest tools; it's about understanding the deeper architectural shifts happening right now.

Here are the major trends I see defining the next phase:

From Chatbots to Autonomous Agents: 
The biggest shift is mastering "Agentic" frameworks (like LangGraph, CrewAI, ADK). It’s no longer just about one model output; it’s about orchestrating systems that can plan, use tools, and execute multi-step tasks autonomously.

Context is King (RAG & Vector Search): 
Models are useless without the right context. The ability to build robust Retrieval-Augmented Generation (RAG) pipelines and manage custom embeddings is crucial for connecting LLMs to private business data effectively.

Full-Stack AI Engineering: 
We are seeing the rise of the "AI Engineer." This means understanding API integrations, tool stacking (Zapier, Make), and building scalable SaaS AI applications from the ground up.

Governance & Safety cannot be an afterthought: 
As these systems become more autonomous, the skills to monitor for hallucination, bias, and ensure guardrails (NeMo, Guardrails AI) become non-negotiable enterprise requirements.

The landscape is moving fast. 

The best way to stay ahead is to start building today.

Which of these areas are you prioritizing for your upskilling journey over the next year?

Only 21 days left in 2025 — and this one decision could change your entire AI career trajectory.

If you’re serious about learning Generative AI — not just talking about it — here’s the move:

From today until December 30, commit just 50 minutes a day using Microsoft’s FREE, beginner-friendly Generative AI learning path. (Link in the comments)

This isn’t another fluffy “learn AI” post.

This is a hands-on, real-world, no-BS roadmap to becoming interview-ready and actually building GenAI applications.

What you’ll learn:
 • How LLMs actually work (far beyond simple prompting)
 • Using OpenAI & Azure APIs to build real projects
 • Integrating GenAI features into modern web apps (Python + TypeScript)
 • Ethical AI + responsible design principles that employers expect

 No expensive bootcamp.
 No perfect timing.
 No prior AI experience required.
Just show up daily.

Learn → Build → Repeat.

By December 30  you could:
 ✔️ Build your first GenAI tool
 ✔️ Speak confidently in AI interviews
 ✔️ Replace doomscrolling with real skills
 ✔️ Position yourself for 2026 roles in AI Engineering, Agentic AI & Applied ML
Momentum beats motivation.

 Start today — future you will thank you.

Agentic AI System Blueprint — More Than Just Tools. 

It’s a Structured Intelligence.

Everyone talks about AI agents.

But very few understand what truly makes them autonomous, reliable, and enterprise-ready.

Most assume it's just prompt chaining, API calling, or tool invocation.

But that’s only one layer — Execution.

The real power of Agentic AI lies in how all its layers work together as a coherent system.

Here’s the full stack that transforms a basic LLM into a self-directed, resilient, and auditable AI agent:

🔹 Planning & Tasks
 Where intelligence begins. Goal definition, task decomposition, coordination, role assignment, and dynamic re-prioritization — this is where agents plan, not just respond.

🔹 Memory & Context
 Short-term context is not enough. Enterprise agents rely on semantic memory, episodic recall, vector databases, knowledge graphs, and temporal context to make decisions based on past experiences — just like humans.

🔹 Execution
 Where most people start — but it's actually the third layer. Tool invocation, function execution, reasoning, API calling, document processing, and multi-step autonomy.

🔹 Monitoring
 Real-time tracking, workflows, agent health checks, logs, metrics, recovery, auditing — because what you cannot monitor, you cannot trust.

🔹 Optimization
 Self-adaptation, feedback loops, reinforcement learning, performance scoring, and reward models enable agents to continuously improve rather than repeat the same mistakes.

🔹 Governance
 Data privacy, compliance audits, guardrails, human validation, explainability, access control — the foundation of trustworthy AI in regulated industries.

🔹 Infrastructure
 The unsung hero — vector DBs, model hosting, GPUs, API gateways, observability, caching, orchestration (Kubernetes/Docker). The plumbing that makes everything scale.

Agentic AI isn’t just prompting.

It is a full-stack architecture.

Most AI agents fail for one simple reason:


They’re built like chatbots, not systems.

The internet tells you:
 “Add tools.”
 “Add memory.”
 “Add RAG.”
That’s not why agents break in production.
They break because nobody defines control.
Here’s the real mental model I use when building AI agents 👇

1. An agent is a decision system, not a response generator
 LLMs are good at choosing actions.
 They are terrible at being left unsupervised.
If your agent can act, you must define:
when it can act
how often
with what confidence
and what happens when it’s wrong

2. Tools are liabilities, not features
 Every tool you add:
increases failure modes
increases blast radius
increases cost
Production agents don’t have “many tools”.
 They have the minimum set required to complete one job.

3. Memory is where agents quietly go off the rails
 Most teams store everything.
 Good agents store decisions, not conversations.
Memory should answer:
“What must this agent remember to make a better next decision?”
Nothing more.

4. Planning is optional. Verification is not.
 Planning looks impressive in demos.
 Verification saves you in production.
Every agent should ask:
“Did this tool call succeed?”
“Does this output meet the contract?”
“Do I need to retry or stop?”
If your agent can’t say no, it’s not autonomous — it’s reckless.

5. Evaluation is the real intelligence layer
 Without evals:
you don’t know if it’s improving
you don’t know if it’s degrading
you don’t know if it’s safe
No evals = no agent. Just vibes.

The hard truth:

 Most “agent frameworks” optimize for demos.
 Real agents optimize for control, observability, and failure handling.

That’s what separates:
- toy agents
- systems that companies trust

The graphic attached isn’t a checklist.

 It’s the minimum architecture for an agent you can sleep on.

Agentic AI isn’t just about retrieval — it’s about reasoning, memory, orchestration, and adaptability.

For years, most people have seen RAG as a simple retrieve → generate cycle.

But as AI systems matured, RAG started evolving into Agentic RAG —where multiple intelligent agents collaborate, reason, use tools, manage memory, and continuously improve.

I sketched this workflow to show how Agentic RAG actually works in practice:

• The Agent Orchestrator coordinates decisions, tools, and workflows

• Search, Vector Store, Code Interpreter, and Context Manager actively work together

• Knowledge isn’t just retrieved — it is interpreted, linked, synthesized, and reused

• Memory isn’t static — it is adaptive, persistent, and context-aware

Knowledge operates at multiple levels:

Sentence → Paragraph → Document → Iterative Reasoning

So the system doesn’t just answer —it remembers, interprets, connects, and evolves.

This blueprint is shaping the next generation of:

 • AI copilots that retain context across sessions
 • Digital assistants that learn user preferences over time
 • CX agents that reason, not just retrieve
 • Enterprise AI systems that comply, explain, and improve at scale

RAG connects data.
Agentic AI connects intelligence.

The Rules of Traffic Management Have Changed 

We’ve spent the last decade mastering the API Gateway. 

It was the perfect gatekeeper for deterministic, request-based traffic. But as we shift toward Agentic AI and LLM-driven architectures, the traditional rules no longer apply.

I recently broke down the critical differences between a standard API Gateway and the emerging AI Gateway. Here is the reality check for anyone building GenAI applications:

1. The Unit of Value has Shifted: In the API world, we measure Requests Per Second (RPS). In the AI world, that metric is insufficient. We need to measure Tokens, Cost, and Context. If your gateway can't count tokens, you can't control your budget.

2. Caching is no longer binary: Standard HTTP caching relies on exact header/path matches. AI Gateways introduce Semantic Caching.
Old Way: User asks "What is the capital of France?" -> Cache Miss -> Backend Call.

New Way: User asks "France's capital city?" -> Semantic Match -> Cached Response. This distinction alone can reduce LLM costs by 30-50%.

3. Security is deeper: We aren't just validating OAuth tokens anymore. We are now defending against Prompt Injection, managing PII redaction inside the prompt payload, and handling fine-grained model access. The gateway needs to be "content-aware," not just a blind pipe.

4. Routing is about Availability & Intelligence: Instead of simple Round Robin or Least Connections, AI Gateways route based on Model Capability. Does this prompt need GPT-4, or is a smaller, cheaper model sufficient? This "Model Routing" is the key to viable unit economics.

You cannot treat LLM traffic like standard REST calls. If you are building for the future, your infrastructure needs to understand the language of the payload, not just the protocol.

Vector search gave LLMs memory.


Graph databases gave LLMs relationships.

But neither could give LLMs real-time reasoning.

That’s the next frontier.

Because agents don't just need content — they need connected knowledge that they can reason over, instantly.

And here’s where the traditional stack fails:

Most graph databases still “walk” through data — one node, one edge, one hop at a time.

Exactly like humans flipping pages in a directory.

That works for analytics.
It collapses for AI agents.

The core idea:
What if graphs stopped behaving like “maps”…
and started behaving like “math”?

That’s the FalkorDB breakthrough.

Instead of hopping from node to node — FalkorDB converts the entire graph into a sparse matrix.

Your data becomes a mathematical object.
And once your graph is math — queries become math too.

Not traversal.
Not step-by-step.
Just matrix computation using linear algebra.

And math doesn’t walk.

It computes.

Which means:

Real-time graph reasoning for agents. At scale.

Why this changes the game for LLMs:

Vector search tells you what is similar.
Graphs tell you what is connected.
But sparse matrix graphs tell you what is structurally meaningful — instantly.
It’s the difference between finding a document…

 …and finding the truth inside a network of relationships.

That's how agents will think.

FalkorDB brings this into the real world:

 🔹 Graphs as sparse matrices — zero traversal overhead
 🔹 Linear algebra-powered queries — orders-of-magnitude faster
 🔹 Redis-native, open-source, lightweight deployment
 🔹 OpenCypher compatible — no need to learn a new language
 🔹 Built specifically for LLM context, agent memory, and reasoning

I tested it — queries that took seconds now feel like function calls.

Agents that relied on retrieval now reason in real-time.

The future isn't LLMs with bigger context windows.
It’s LLMs with smarter knowledge structures.

And frameworks like FalkorDB will power that shift.

I’ve shared their GitHub link in the comments — explore it, run it, stress it.

It feels like where Agent Memory is heading.

Agentic AI is not a platform, not a library, and not a single framework.

It is an architecture ecosystem.

Most conversations around Agentic AI focus on tools—LangChain, AutoGen, CrewAI, DSPy, LangGraph, etc.

Useful, yes. But tools are not the system.

Real Agentic AI—when implemented in enterprise consists of layered capabilities.

Just like cloud has layers (IaaS / PaaS / SaaS), Agentic AI has its own foundational stack.

I have mapped the Agentic AI technology stack into seven layers.

Not theoretical. Not hype.

This is what is actually used to operationalize multi-agent systems.

Memory Systems:
Vector memory, episodic memory, shared memory, structured memory, dynamic context management.
Without adaptive memory, it's not an agent system—it's a chat interface.

Tool Integration & Reasoning Engines:
Function calling, retrieval, symbolic reasoning, planning, execution tracking, guardrails, API governance.

Multi-Agent Collaboration Frameworks:
Task specialization, negotiation, delegation, dependency resolution, arbitration, goal alignment.

Workflow Orchestration & Autonomy Platforms:
State management, decision loops, human-in-the-loop escalation, routing logic, fallback strategies.

Evolution, Feedback & Debugging Systems:
Self-improvement, log-based learning, meta-evaluation, execution trace analysis, agent QA.

Deployment & Scaling Infrastructure:
Versioned agents, state checkpoints, event streams, reproducibility, sandboxing, inference routing.

Monitoring, Governance & Compliance:
Policy enforcement, reliability, auditability, red-teaming, safe autonomy, risk monitoring.
The moment you start thinking in layers, Agentic AI shifts from “which tool should I use?” to“which layer am I designing, and which one is missing?”

That is where real architecture begins.

This visual is a snapshot of the Agentic AI Technology Landscape — 2025 Edition.

Each tool falls into a specific capability layer, not a category.

If you design, evaluate, or deploy agent systems, this layered view changes how you make decisions.

Which layer do you think is currently the weakest across the ecosystem?

Not all multi-agent systems think the same way.

Beneath the orchestration and workflow layers lie patterns of reasoning — architectural choices that determine how agents learn, adapt, and collaborate.

There are many ways to design such systems, but when viewed through the lens of how intelligence organizes itself, as Kence Anderson introduced in his book “Designing Autonomous AI (O’Reilly), three foundational patterns stand out:

1. The Strategy Pattern
 Intelligence through adaptation.
 Each agent develops distinct strategies for different contexts. An orchestrator selects and executes the optimal one, much like a coach adjusting tactics mid-game.

2. The Functional Pattern
 Intelligence through process.
 Agents operate in a structured sequence, each mastering a specific function — analyzing, planning, or executing — where success at one stage enables the next.

3. The Perception Pattern
 Intelligence through awareness.
 Agents incorporate sensory or perceptual capabilities — classification, prediction, and contextual understanding — enhancing decision quality and autonomy.

Together, these patterns illustrate that multi-agent systems are not simply task automation frameworks.

They represent an emerging form of distributed cognition — systems capable of reasoning, coordinating, and improving collectively.

The real question isn’t how many agents can be deployed,but how intelligently they are designed to think together.

After designing more than 200 agents at Microsoft and writing THE book on Autonomous AI, Kence is putting these principles into practice at AMESA(Advanced Modular Enterprise Systems of Autonomy)— creating an AI proving ground where teams of agents can be designed, trained, and validated safely before deployment.
It’s a significant step forward:

 from experimenting with prompts to engineering intelligence,

 from simulating autonomy to proving it in real-world systems.
Thanks to AMESA for sponsoring this post.

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