Kedeisha Bryan

Founder at Data in Motion LLC | Senior Data Visualization Consultant | Navy Veteran | Top 30 Women in Data | Building the best skill & career development platform for the data community

You want a better career. But you’re stuck

No tech degree.

No portfolio.

No idea how to stand out.

That’s where I come in.

I’m Kedeisha Bryan, founder of Data in Motion. I help career changers launch $100k analytics careers without going back to school.

How? By teaching the exact skills employers want: SQL, Excel, Power BI, Tableau, Python, PowerPoint, storytelling and real-world pro-jects that prove your value.

By coaching you through interviews, negotiations, and personal branding.

By giving you the confidence and tools to compete with anyone in the market.

The results?
✔ 60,000+ in the Data in Motion community.
✔ Thousands of students hired at Microsoft, Chase, Roku, and more.
✔ Career changers moving from admin, customer support, teaching, even warehouse jobs… into high paying analyst roles.

A few years ago, I was delivering pizzas for $8 an hour.

Today, I’m a self-taught data scientist who’s helped thousands transform their careers.

I know the struggle and I know the path out.

If you’re ready to land a data role in the next 6 months, let’s talk.

Ready to land your dream job in data?

Founder & CEO

Online bootcamp and interview preparation program to kickstart a person's data career in 5 months without going back to school

Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

AI Agent vs Agentic AI

Most people use the terms AI Agent and Agentic AI like they mean the same thing.

They don’t.

The difference isn’t just semantic.
It’s architectural.

Here’s how the tech stack evolves from AI Agent → Agentic AI 👇

1. Intelligence models
- AI Agent typically relies on a single LLM with prompt → response workflows.
- Agentic AI moves toward multi-model reasoning, planner–executor setups, and hybrid inference across systems.

2. Architecture & frameworks
- AI Agent often follows a single-agent, linear execution flow.
- Agentic AI introduces multi-agent systems, goal-driven workflows, and orchestration frameworks like LangGraph, CrewAI, or AutoGen.

3. Memory systems
- AI Agent works with session memory, short-term embeddings, and basic caches.
- Agentic AI adds long-term memory layers, episodic + semantic memory, knowledge graphs, and vector databases.

4. Tool usage & actions
- AI Agent uses predefined tools and function calling triggered by users.
- Agentic AI autonomously selects tools, plans multi-step executions, interacts with environments, and uses structured tool registries.

5. Knowledge & retrieval
- AI Agent typically uses basic RAG pipelines with static retrieval.
- Agentic AI evolves into adaptive RAG, context prioritization, hybrid search, and continuously updated knowledge graphs.

6. Orchestration & workflows
- AI Agent runs sequential flows and simple backend automation.
- Agentic AI uses orchestration engines, planning loops, event-driven workflows, and reflection cycles.

7. Decision making
- AI Agent is reactive and prompt-driven.
- Agentic AI is goal-oriented, with planning, self-evaluation, and iterative reasoning loops.

8. Deployment
- AI Agent is often deployed as chatbots, copilots, or API-based assistants.
- Agentic AI becomes autonomous platforms, digital workforce agents, and persistent execution systems.

9. Monitoring & observability
- Both need logs, monitoring, and error tracking but Agentic AI requires deeper analytics, response monitoring, and system-level feedback loops.

10. Learning & improvement
- AI Agent improves through prompt iteration and occasional fine-tuning.
- Agentic AI evolves through continuous feedback pipelines, performance adaptation, and evaluation frameworks.

AI Agent = intelligent responder.
Agentic AI = autonomous system with goals, memory, tools, and orchestration.

One answers questions.
The other executes objectives.

Are you building smarter responses or autonomous systems?

Anthropic made Learning AI easier.

They’ve released a complete set of FREE courses designed to help you understand Claude, AI agents, APIs, and the Model Context Protocol (MCP) - the foundation behind modern AI applications and agent systems.

This learning path covers everything from beginner concepts to advanced agent development workflows.

Here’s what you can learn:

• Claude 101 - Understand core features and everyday AI workflows
• Claude Code in Action - Use Claude to accelerate development tasks
• AI Fluency: Framework & Foundations - Learn how to work effectively with AI
• Building with the Claude API - Develop real AI-powered applications
• Introduction to Model Context Protocol (MCP) - Connect AI with tools and data
• MCP Advanced Topics - Deep dive into advanced implementations
• Claude with Amazon Bedrock - Deploy Claude in AWS environments
• Claude with Google Cloud Vertex AI - Production AI workflows on GCP
• Introduction to Agent Skills - Build reusable AI agent capabilities

These courses are especially useful if you want to learn:
AI Agents • MCP • LLM Applications • AI Automation • Developer Workflows

No fluff.
No paywall.
Just practical AI education directly from the company building Claude.

Get all the courses here:
https://lnkd.in/gAmrrsBK

If you’re serious about understanding how modern AI systems actually work, this is one of the best free starting points available right now.

Save this for later - you’ll want to come back to it.

Claude vs ChatGPT ?

It’s not a rivalry, It's a toolkit decision.
Both are powerful. Both have blind spots.

The smartest operators aren't loyal to one - they know exactly when to use which.

Here's the breakdown : 

Long Documents & Research
Claude → Analyzing contracts, reviewing large codebases, multi-document reasoning, structured research synthesis.

ChatGPT → Summarizing quickly, extracting highlights, generating reports, visualizing insights.
Need depth? Claude. Need speed? ChatGPT.

Coding & Development
Claude → Architecture reviews, refactoring complex logic, explaining systems deeply, debugging reasoning-heavy code.

ChatGPT → Rapid prototyping, boilerplate generation, built-in Python and browser tools, building agents with APIs.
Need to understand the code? Claude. Need to ship fast? ChatGPT.

AI Agents & Automation
Claude → Safety-critical tasks, high-stakes reasoning, enterprise deployments, controlled tool execution.

ChatGPT → Production agents, function calling, quick API integrations, leveraging ecosystem tools.
Need control and safety? Claude. Need speed and ecosystem? ChatGPT.

Business & Strategy
Claude → Policy documents, governance frameworks, risk analysis, strategic breakdowns.

ChatGPT → Market research synthesis, presentation drafts, data analysis with tools, growth strategy ideation.
Need structured thinking? Claude. Need fast outputs? ChatGPT.

Creativity & Content
Claude → Deep essays, thought leadership writing, analytical storytelling, long-form structured content.

ChatGPT → Brainstorming, social media content, image generation, multimodal creative tasks.
Need depth and voice? Claude. Need volume and variety? ChatGPT.

The Quick Decision Rule:
Choose Claude when:
- Depth beats speed.
- Structure beats creativity.
- Safety beats flexibility.
- Context window matters.

Choose ChatGPT when:
- Tools matter.
- Multimodal work is required.
- Fast iteration is the priority.
- Broad ecosystem integration is needed.

The final summary nobody argues with:
Claude = Deep thinking. Structured reasoning. Enterprise safety.
ChatGPT = Versatile. Tool-rich. Multimodal productivity engine.

Stop asking which AI is better.
Start asking which AI is better for this specific task.
That shift alone will change how much you get done.

Google just quietly released an AI tool for every layer of how humans create things.

Research. Design. Code. Video. Agents. Models.

All of it. At once.
Connected. Intentional. Ecosystem-scale. 

The tools nobody is talking about enough:

Jules - an asynchronous coding assistant that works across entire repositories, not just individual files. It understands the broader structure of a project and helps manage complex codebases.

Google A2A - a multi-agent communication protocol designed to let AI agents collaborate across different frameworks. This is infrastructure for the next generation of agent systems.

Stitch - converts prompts directly into complex UI layouts, turning ideas into interface designs within minutes. A major shift for how design workflows could work.

FileSearch API - a configurable RAG pipeline accessible through a single API call. What previously required weeks or months of engineering can now be integrated almost instantly.

Flow - a cinematic storytelling platform that lets creators generate scenes, narratives, and video content with AI-driven workflows.

Gemini 3 Pro - Google’s most advanced reasoning model so far, designed for deeper analysis, complex tasks, and next-generation AI systems.

This isn't a product launch.

It’s a full-stack AI ecosystem.

From foundational models → to research tools → to design → to development → to autonomous agents → to video creation.

Everything sits on top of the same AI foundation.

That means ideas can move from research → design → code → content faster than ever before.

The real strategy here isn’t individual tools.
It’s ownership of the entire creation pipeline.

And when one company controls every layer of that pipeline, the innovation speed compounds.

The AI race isn’t just about better models anymore.

It’s about who builds the ecosystem developers, creators, and companies build on.

People still use AI like a chatbot.
The real power starts when it becomes your execution layer.

Claude Code isn’t just another AI tool.
It acts like an operating system for building, testing, and shipping real work.

Instead of writing prompts and copying outputs, you can actually run workflows, analyze codebases, connect tools, and automate tasks from one place.

That’s the shift most teams haven’t fully understood yet.

Traditional AI tools help you think faster.
Claude Code helps you execute faster.

It moves through a structured loop:
analyze → plan → build → automate → scale.

You can connect repositories, files, databases, and internal tools.
Run commands.
Generate code.
Debug systems.
Automate repetitive work.
And build reusable skills that keep improving over time.

It also works across environments, which means you’re not jumping between ten different tools to get one workflow done.

The biggest difference?
It behaves less like a chatbot and more like a technical teammate that can actually take action.

When used properly, it becomes a working layer on top of your stack — not just a place to ask questions.

Most people experiment with AI.
Few build repeatable systems with it.

That’s where the real leverage is.

AI becomes valuable when it moves from answering questions to executing workflows.

Start treating it like an operating system for work, not just a prompt tool.

The agent gave a brilliant answer.
Then someone asked, “Where did that data come from?”
Silence. That’s when you realize intelligence needs infrastructure.

Agentic AI isn’t just reasoning.
It’s retrieval, validation, lineage, governance, and monitoring - all working quietly in the background.

Here’s what that foundation looks like:

- Data Ingestion
Pulls in structured and unstructured sources, standardizes formats, and attaches context before anything reaches an agent.

- ETL / ELT Pipelines
Transforms, validates, and loads data into usable systems so downstream workflows aren’t built on chaos.

- Data Versioning
Tracks changes, creates reproducible snapshots, and enables rollback when something breaks or drifts.

- Vector Pipelines
Chunks content, generates embeddings, and indexes it for semantic retrieval in RAG and agent workflows.

- Metadata Management
Registers datasets, records ownership, and enables search and observability across the ecosystem.

- Data Governance
Enforces policies, access controls, and compliance rules to prevent misuse and ensure accountability.

- Data Warehouse / Lakehouse
Stores refined datasets that power analytics, feature engineering, and intelligent querying.

- Streaming Data
Processes real-time events so agents can react, trigger actions, and operate dynamically.

- Data Lineage
Maps where data originated, how it transformed, and who depends on it.

- Data Quality & Reliability
Applies validation checks, detects anomalies, and keeps pipelines healthy under pressure.

At the center is the agent itself -
retrieving context, reasoning over data, planning actions, learning from feedback.

But without the layers beneath it,
even the smartest answer becomes a liability.

Intelligence is impressive.
Traceable, governed intelligence is enterprise-ready.

If someone asks where your agent’s answer came from, can you show them?

If you learned AI even three years ago, you’re already studying history.

The conversation has moved far beyond models and prompts. What we’re witnessing now isn’t just incremental improvement, it’s a fundamental shift in how intelligence is built, deployed, and experienced inside real systems.

AI didn’t suddenly appear with ChatGPT.
It evolved through decades of breakthroughs, each solving the limitations of the era before it - gradually transforming software from rule execution into adaptive intelligence.

Understanding that evolution is what reveals where AI is actually heading next.

From Rules → Learning
AI moved from hard-coded logic toward systems capable of learning patterns directly from data instead of predefined instructions.

From Learning → Representation
Deep learning and transformers introduced contextual understanding, enabling language comprehension, perception, and multimodal intelligence at scale.

From Models → Platforms
Foundation models and Retrieval-Augmented Generation turned AI into reusable platforms connected to dynamic, real-world knowledge sources.

From Assistants → Agents
AI progressed from answering questions to planning actions, reasoning through problems, and executing complex multi-step workflows autonomously.

Where AI Is Heading (2026+)
Intelligence is moving toward edge computing, embodied AI, persistent memory, and systems embedded seamlessly into everyday environments.

Every generation of AI didn’t replace the previous one, it stacked on top of it.

Many organizations struggle today because they attempt to adopt the newest layer without understanding the foundations underneath. Real advantage comes from recognizing how these layers connect:

Rules → Learning → Reasoning → Agents → Autonomous Systems

We’re not reaching the end of AI evolution.

We’re entering the phase where intelligence stops being a feature and becomes a system layer woven into everything we build.

#AI

Anthropic’s latest report highlights a shift many enterprises still underestimate:

AI agents have moved from experimentation into core operations and adoption is happening faster and wider than expected.

The report tracks deployments across 15+ business domains. What stands out isn’t just scale, it’s how deeply agents are embedded into real workflows across the enterprise.

This isn’t just a tech trend. It’s an operational shift.
The companies pulling ahead aren’t spending more - they’re deploying smarter, where agents remove friction and accelerate outcomes.

Here’s where enterprises are actually putting agents to work:

• Software Engineering - Writing, reviewing, testing, and debugging code while accelerating delivery cycles.
• Back-Office Automation - Eliminating manual workflows like data entry, processing, and internal operations.
• Marketing & Copywriting - Scaling content creation, personalization, and campaign execution end-to-end.
• Sales & CRM - Automating prospecting, follow-ups, and CRM workflows so teams focus on closing.
• Finance & Accounting - Reconciliation, anomaly detection, reporting, and audit-ready operations.
• Data Analysis & BI - Turning raw data into insights, dashboards, and narratives instantly.
• Customer Service - Resolving tickets autonomously while escalating only complex edge cases.
• Academic Research - Synthesizing literature, accelerating reviews, and mapping knowledge faster.
• Cybersecurity - Monitoring threats continuously and triggering real-time automated responses.
• Document & Presentation Creation - Structuring reports, slides, and outputs directly from inputs.
• Education & Tutoring - Personalized learning, real-time assistance, and adaptive assessments.
• Medicine & Healthcare - Clinical documentation, workflows, and patient communication support.
• Legal - Contract review, due diligence acceleration, and compliance workflows at scale.
• E-commerce Operations - Managing listings, pricing, inventory signals, and customer support.
• Gaming & Interactive Media - Driving adaptive narratives and real-time interactive experiences.
• Travel & Logistics - Route optimization, bookings automation, and supply chain coordination.

The pattern is clear:

Agents aren’t a single-use tool - they’re becoming infrastructure across every function and industry.

The question isn’t whether your business will use agents. It’s whether you’ll move fast enough to stay competitive.

Which domain are you building in?

If you think an AI agent is just an LLM connected to tools, you’re only seeing the surface.

What actually powers a production-grade AI agent is a layered intelligence architecture - where memory, reasoning, governance, and infrastructure work together to transform responses into reliable autonomous behavior.

Modern AI systems are no longer single models.
They are orchestrated systems designed to think, plan, adapt, and operate safely at scale.

This visual breaks down the internal anatomy of a real-world AI agent across three evolving layers:

From Execution → Coordination
The Basic Layer handles core operations — processing inputs, orchestrating prompts, invoking models, executing tools, managing errors, and delivering structured responses.

From Coordination → Reasoning
The Intermediate Layer introduces structured intelligence. Agents decompose tasks, plan execution strategies, validate outputs, manage context, monitor costs, and maintain observability for reliable performance.

From Reasoning → Autonomy
The Advanced Layer enables true production intelligence through long-term memory, multi-agent collaboration, governance guardrails, adaptive learning, self-reflection, and scalable deployment infrastructure.

Each layer doesn’t replace the previous one — it builds on top of it.

Organizations struggle with AI adoption not because models are weak, but because architecture is incomplete. Real AI capability emerges only when these layers operate as a connected system:

Execution → Reasoning → Governance → Autonomy

We’re moving toward a future where AI isn’t an application feature.

It becomes an operational layer embedded into every digital system we build.

Gemini just launched Embedding 2 !!!

And this quietly changes how we build AI systems.

This isn’t just another model update.
It’s Google’s first natively multimodal embedding model - meaning text, images, video, audio, and documents are all mapped into a single shared understanding space.

And that unlocks a completely different level of intelligence.

🔹 What makes Gemini Embedding 2 different?
Instead of handling data separately, it understands relationships across modalities.
You can now input text + image + audio in a single request and the model captures the full context.

This means:
• Search using an image → retrieve relevant videos
• Query with text → find exact moments inside audio
• Combine inputs → get deeper semantic meaning

🔹 Key capabilities you should know
• Text: Up to 8192 tokens context
• Images: Up to 6 images per request
• Video: Up to 120 seconds supported
• Audio: Direct embedding (no transcription needed)
• Documents: PDFs up to 6 pages

Plus, it supports 100+ languages, making it globally usable out of the box.

🔹 Why this matters for AI builders
This model simplifies what used to be complex pipelines:

Before:
Text embeddings + image models + speech-to-text + vector search stitching

Now:
One model → one embedding space → unified retrieval

That’s a massive reduction in system complexity.

🔹 Where this will be used (immediately)
• RAG systems with multimodal context
• Semantic search across media libraries
• Recommendation systems using mixed data
• Content clustering and classification
• Enterprise knowledge retrieval (docs + media together)

🔹 Performance leap
Early benchmarks show strong improvements across text, image, and video tasks.
Real-world use cases are already hitting 85%+ recall in text-to-video retrieval.

That’s not incremental, that’s a shift in capability.

🔹 Technical edge (important)
Gemini Embedding 2 uses Matryoshka Representation Learning (MRL)
→ You can scale embedding dimensions (3072 → 1536 → 768)
→ Balance between performance and storage cost

This is huge for production systems.

🔹 The bigger picture
We’re moving from:
Single-modality AI → Multimodal intelligence layers

From:
“Search data” → “Understand data across formats”

Embedding models are no longer just backend tools -
they’re becoming the foundation layer of AI systems.

If you’re building anything in:
AI Agents, RAG pipelines, enterprise search, or automation systems

This is something you should be paying attention to.

Because the next generation of AI won’t just read text - it will understand everything.

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Greg Coquillo

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The Battle

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