Adam Danyal, 86-90 Paul Street, London (2026)

12/06/2026

Most AI agent failures are context failures.

The source post breaks down the 3 context layers every serious AI agent needs.

MCP, RAG, and Skills are not interchangeable.

They solve different failure modes.

→ MCP solves the tool-access problem.

Without MCP, every Slack, Qdrant, or Brave Search connection can become custom API work.

With MCP, the agent uses a standardized protocol.

The basic flow:

User query → MCP Client → MCP Server → external tool → final response.

Use MCP when the agent needs to act across tools and services.

The diagnostic check:

If every new integration needs fresh code, you likely need MCP.

→ RAG solves the knowledge problem.

Without RAG, an LLM answers from training data and can hallucinate.

With RAG, the agent retrieves relevant information first.

The basic flow:

Data sources → chunking → embeddings → Vector DB → retrieval → LLM output.

Use RAG when the agent needs accurate answers from changing documents, databases, PDFs, or websites.

The diagnostic check:

If answers depend on private or dynamic knowledge, you likely need RAG.

→ Skills solve the repeatable-action problem.

Without Skills, you keep stuffing the same instructions into every prompt.

With Skills, the agent loads reusable actions only when needed.

The basic flow:

User request → Skill Manager → Skill Selection → Git, Docker, Python Interpreter, or Shell → result.

Use Skills when the agent needs repeatable workflows without repeated instructions.

The diagnostic check:

If prompts keep getting longer just to repeat process rules, you likely need Skills.

✅ MCP connects the agent to tools.

✅ RAG connects the agent to knowledge.

✅ Skills connect the agent to reusable actions.

The real mistake is treating “agent context” as one thing.

It is a stack.

Before building another agent, map the failure mode first.

If it cannot access tools, add MCP.

If it cannot trust answers, add RAG.

If it cannot repeat work, add Skills.

And weak context makes weak agents.

Which layer is your team missing right now?

12/06/2026

Most weak ChatGPT outputs start with weak instructions.

ChatGPT is rarely the bottleneck.

The prompt is.

The source post is useful because it turns vague requests into repeatable prompt codes.

Use these as shortcuts when you need better output fast.

→ Simplification & Teaching

✅ ELI5: ask ChatGPT to explain a topic like you are 5 when the first answer is too abstract.

✅ Break it down: force the answer into parts before asking for recommendations.

✅ Use analogies: compare a technical idea to a real-world object or workflow.

✅ Scaffold it: ask for a step-by-step learning structure before studying a new subject.

✅ Summarize and explain: shorten a dense document, then clarify the important parts.

→ Professional Roles

✅ Act as a lawyer: shift ChatGPT into legal-tone analysis before reviewing contract language.

✅ Act as a resume expert: improve a CV or rewrite bullets for a specific role.

✅ Act as a business coach: pressure-test a startup, growth, or operating decision.

✅ Act as a programmer: explain code, debug logic, or propose implementation steps.

✅ Act as a marketer: turn a rough idea into positioning, copy, or content angles.

→ Formatting & Style

✅ Bullet points: make ChatGPT return short, scannable output.

✅ TL;DR: compress a long answer into the decision-relevant version.

✅ Output as JSON: convert messy information into structured data.

✅ Table format: compare options, tradeoffs, owners, or priorities side by side.

✅ Checklist: turn advice into actions you can execute.

→ Utility & Control

✅ Don’t answer until I say Go: pause ChatGPT while you load context.

✅ Stay in character: preserve a role or voice across a longer task.

✅ Be brutally honest: ask for direct critique when the draft is weak.

✅ Create a system prompt: set the model’s mindset before repeated work.

✅ Be concise: reduce output when speed matters more than detail.

The pattern is simple:

Tell ChatGPT the role.

Tell it the format.

Tell it the level of detail.

Then control the output before it drifts.

Make the model easier to steer.

Which prompt code would improve your workflow today?

10/06/2026

AI talent is becoming an org-design problem, not just a hiring problem.

The companies moving fastest are not hiring one “AI person.”

They are building an AI operating model.

The source post called out a hard problem:

94% of CEOs say AI skills are priority #1.

90% of enterprises still cannot find enough AI talent.

That gap creates a practical question for leaders:

Which AI roles do we actually need first?

→ Management roles set direction.

✅ Chief AI Officer owns AI strategy, governance, and executive accountability.

✅ Head of Applied AI turns use cases into production outcomes.

✅ AI Product Manager manages the AI product lifecycle end to end.

✅ Responsible AI Lead reduces deployment risk before trust problems scale.

✅ Director of AI Transformation connects roadmap, delivery teams, and change management.

→ Technical roles build and secure the system.

✅ AI Architect designs scalable AI system architecture.

✅ Model/ML Engineer builds and deploys machine learning models.

✅ AI Application Developer integrates AI into business software.

✅ AI Redteam Engineer tests model weakness before customers or regulators find it.

✅ Data Engineer keeps the pipelines reliable enough for AI to work.

→ Business roles turn AI into operating leverage.

✅ AI Accounting Analyst improves forecasting, reconciliation, and reporting workflows.

✅ AI-Powered Auditor detects anomalies, fraud signals, and control weaknesses.

✅ AI Compliance Analyst monitors regulations and flags AI-driven risk.

✅ AI HR Data Analyst converts workforce data into better people decisions.

✅ AI Customer Success Manager uses AI insights to predict churn and improve adoption.

The diagnostic is simple:

If AI is still “owned by IT,” you probably have a strategy gap.

If experiments do not reach production, you probably have a delivery gap.

If teams cannot trust outputs, you probably have a governance gap.

If business units do not use the tools, you probably have an adoption gap.

AI hiring is shifting from isolated specialists to connected role families.

Leaders who map the gaps now will move faster later.

The question is not “who can prompt?”

It is “who owns strategy, delivery, risk, and adoption?”

Which role is missing from your AI roadmap right now?

10/06/2026

Most AI mistakes start with picking the wrong model.

Here is the founder-level way to choose between ChatGPT, Gemini, and Copilot.

✅ ChatGPT is your creation engine.

→ Use ChatGPT when the task starts with a blank page.

↳ Draft the strategy memo.

↳ Turn messy notes into a plan.

↳ Build a prompt system for repeatable work.

🎯 Diagnostic check:

If the output needs judgment, language, structure, or speed, start with ChatGPT.

✅ Best before state:

You have an idea, meeting notes, customer research, or a rough workflow.

✅ Best after state:

You have a clear draft, decision tree, checklist, or reusable operating system.

❌ Weak spot:

Do not treat ChatGPT as a source of truth without review.

→ Ask it to cite assumptions.

→ Compare sensitive answers against primary sources.

→ Use expert prompts when the task is niche.

✅ Gemini is your analysis engine.

→ Use Gemini when the task depends on files, research, logic, or multimodal review.

↳ Review a long PDF.

↳ Compare product screenshots.

↳ Analyze data-heavy material inside Google Workspace.

🎯 Diagnostic check:

If the task needs depth across large context, Gemini usually deserves the first pass.

✅ Best before state:

You have documents, visuals, spreadsheets, or research fragments.

✅ Best after state:

You have patterns, summaries, risk areas, and a cleaner analytical view.

❌ Weak spot:

Gemini can feel heavy for simple office work.

→ Use it when complexity justifies the setup.

✅ Copilot is your ex*****on engine.

→ Use Microsoft Copilot when the work lives inside Word, Excel, Outlook, Teams, or Microsoft 365.

↳ Summarize a thread.

↳ Draft the follow-up.

↳ Pull context from company files.

↳ Build internal agents with Copilot Studio.

🎯 Diagnostic check:

If the task is repetitive, operational, and inside Microsoft systems, Copilot is the practical choice.

✅ Best before state:

Your team is stuck in admin loops and scattered updates.

✅ Best after state:

Routine work gets faster, cleaner, and easier to delegate.

❌ Weak spot:

Copilot is not always the strongest tool for deep reasoning or creative exploration.

The simple rule:

ChatGPT creates.

Gemini analyses.

Copilot executes.

The edge is not owning every model.

It is matching the model to the job before time gets wasted.

Which one should your team use less often?

10/06/2026

Most people use Copilot in one app and leave the real leverage untouched.

Copilot gets more useful when you treat Microsoft 365 as one workflow.
Not six separate apps.
One connected work system.

The pattern is simple:
short prompt
specific output
clear next action
That is where the time savings actually starts to compound.

→ Word
Use it when a messy draft needs a cleaner executive version.
Prompt: “Rewrite this in a professional tone and reduce it to 5 key points.”
Before: long document with unclear emphasis.
After: tighter summary your team can review quickly.

→ Excel
Use it when the spreadsheet is full of numbers but short on insight.
Prompt: “Analyze this data and explain the top 3 trends in simple language.”
Ask for the trend, the likely cause, and the decision it supports.

→ PowerPoint
Use it when a document needs to become a presentation fast.
Prompt: “Turn this document into a 7-slide presentation with speaker notes.”
Then check whether each slide has one point, one visual idea, and one takeaway.

→ Outlook
Use it when a thread is too long to read from the top.
Prompt: “Summarize this email thread and draft a polite reply.”
Add the tone you want: direct, warm, firm, or executive.

→ Teams
Use it when meetings create noise but decisions get lost.
Prompt: “Summarize the key decisions and action items from this meeting.”
Ask for owners, deadlines, unresolved questions, and blockers.

→ OneNote
Use it when notes are scattered across meetings, ideas, and tasks.
Prompt: “Organize these notes into clear sections and extract all action items.”
This turns raw capture into a working operating document.

The mistake is asking Copilot to “help with this.”
That prompt is too vague.

Give it the app.
Give it the output.
Give it the constraint.

Microsoft 365 already holds the work.
Copilot helps when you give it a precise job inside that work.

Which app would save you the most time this week?

10/06/2026

Most people use one Claude. The leverage is knowing which Claude to use.

Claude is not one workflow anymore.

It is three different ways to get work done.

The mistake is opening Claude Chat for everything.

Use the right version for the job.

That choice changes the output, the review process, and the amount of manual cleanup your team has to do later.

01 Claude Chat
→ Use it when you need thinking, writing, research, or a quick answer.
↳ Setup is zero minutes. Open the tab and start typing.

Best for:
→ Drafting emails, summarising documents, brainstorming ideas, and explaining complex topics.

Watch the limit:
→ Chat ends when you close the tab.
↳ Nothing durable gets shipped unless you move the output somewhere else.

Pick Claude Chat when:
→ You want to think something through fast.

02 Claude Code
→ Use it when you want a working tool, app, automation, or codebase change.
↳ Setup is about 15 minutes with a one-time terminal install.

Best for:
→ Building a website, creating a custom tool, automating a workflow, or debugging existing code.

Watch the limit:
→ Token costs can add up fast.
↳ Outputs still need review before production use.

Pick Claude Code when:
→ You want something real delivered, not just advice about what to build.

03 Cowork
→ Use it when Claude needs to work inside your actual computer.
↳ Setup is about 10 minutes: download the desktop app and connect folders.

Best for:
→ Organising folders, extracting data from PDFs, filling spreadsheets, and running cross-app workflows.

Watch the limit:
→ Desktop only.
↳ Tasks can stop if the app closes or the computer sleeps.

Pick Cowork when:
→ You want repetitive file and app work done without coding.

Simple rule:

Chat thinks.

Code builds.

Cowork operates.

That distinction matters.

Using the wrong Claude creates extra handoffs.

Using the right Claude turns one prompt into actual progress.

Which one are you underusing right now?

10/06/2026

Choosing the right AI tool should not feel random.

Most teams waste time asking which AI tool is “best.”

That is the wrong question.

The better question is:

Which tool fits this job?

Here is the simple breakdown:

→ ChatGPT
Best for everyday productivity.

Use it when you need writing, brainstorming, coding, planning, or problem-solving.

Diagnostic check:
If the task starts as a messy idea and needs structure, start here.

Clear consequence:
You move from blank page to usable draft faster.

→ Grok
Best for real-time trends and current context.

Use it when your work depends on social media, breaking topics, fast commentary, or what people are reacting to now.

Diagnostic check:
If timing matters more than polish, Grok is the better fit.

Clear consequence:
You avoid building content around stale context.

→ Gemini
Best for Google Workspace work.

Use it when your workflow already runs through Docs, Sheets, Slides, Gmail, or Drive.

Diagnostic check:
If the source material is sitting inside Google tools, keep the AI inside that environment.

Clear consequence:
Less copying, less switching, more connected work.

→ Claude
Best for deep reading and reasoning.

Use it for long reports, policy documents, research papers, complex briefs, and careful summarisation.

Diagnostic check:
If the document is long and the details matter, Claude is usually the cleaner choice.

Clear consequence:
You get better synthesis instead of shallow summaries.

→ Perplexity
Best for research and fact-checking.

Use it when you need source-backed answers, citations, comparisons, or quick verification.

Diagnostic check:
If accuracy needs evidence, do not rely on a generic answer.

Clear consequence:
You reduce the risk of confident but unsupported outputs.

The goal is not to use every AI tool.

The goal is to stop using one tool for every job.

Pick the tool based on the task.

That is where the leverage starts.

Which tool do you reach for first?

10/06/2026

Claude only becomes useful when you train it on real work.

Here is a simple 7-day plan to make Claude part of your workflow.

Day 1 → Set up your AI workspace
Create your Claude account.
Choose the latest Claude model.
Customize your instructions.
Give Claude one real task from your work.

Day 2 → Build your personal assistant
Tell Claude who you are.
Explain your goals and workflow.
Share examples of your work.
Create a default assistant prompt.

Day 3 → Teach Claude your style
Upload writing samples.
Define your tone and voice.
List words and phrases you avoid.
Create reusable content templates.

Day 4 → Connect your knowledge
Upload key documents.
Add project briefs.
Share SOPs and playbooks.
Build a central knowledge base.

Day 5 → Work on real projects
Draft a report.
Analyze a spreadsheet.
Summarize a meeting.
Complete one task end-to-end.

Day 6 → Scale production
Batch-create content.
Repurpose long-form assets.
Generate variations.
Build content pipelines.

Day 7 → Master Claude
Create custom projects.
Build specialized assistants.
Test advanced features.
Make Claude part of your daily workflow.

The mistake most people make:
They test Claude with random prompts.
Then they decide it is “not that useful.”

That is the wrong test.

A better test:
Give Claude one recurring workflow.
Give it context.
Give it examples.
Give it constraints.
Then measure whether the work gets faster or cleaner.

Use this diagnostic:
If Claude needs the same correction 3 times, your instruction is missing.
If Claude guesses, your source material is missing.
If Claude rambles, your output format is missing.

You do not need 100 prompts.
You need one workflow that actually matters every single working week.

The leaders who win with AI will not be the ones who try every tool.
They will be the ones who turn useful tools into repeatable systems.

Start with Claude.
Start with one week.
Start with one real workflow.

What task would you train Claude on first?

10/06/2026

The AI stack you used last year is already getting replaced.

The new advantage is knowing which tool replaces which workflow.

Use this as a practical replacement map.

→ Google Search → Perplexity
Use it when you need direct answers, sources, and fewer open tabs.
Consequence: research gets faster, but lazy verification gets riskier.

→ ChatGPT → Claude
Use Claude when long context, writing quality, or structured reasoning matters.
Consequence: prompts need clearer goals, not just quick questions.

→ Reading PDFs → NotebookLM
Drop reports, docs, and transcripts into NotebookLM before summarizing.
Consequence: your team can query the source instead of guessing from memory.

→ Adobe Illustrator → Ideogram 2.0
Use Ideogram for fast concept visuals, text-heavy graphics, and ad variants.
Consequence: design cycles move from hours to minutes.

→ Adobe Photoshop → Nano Banana
Use it for fast image edits, product mockups, and visual experiments.
Consequence: rough creative testing no longer needs a full design pass.

→ PowerPoint → Gamma
Use Gamma when the first version of a deck matters more than pixel control.
Consequence: strategy docs can become presentable faster.

→ Keyboard → Wispr Flow
Use voice for first drafts, meeting notes, and messy thinking.
Consequence: the bottleneck shifts from typing speed to clarity.

→ Microsoft Excel → Julius AI
Use Julius AI when the task is analysis, explanation, or chart exploration.
Consequence: non-technical operators can ask better data questions.

→ Google Chrome → Arc Browser
Use Arc when tabs, research trails, and workspaces need structure.
Consequence: browsing becomes an operating system for knowledge work.

→ Adobe Premiere → OpusClips
Use OpusClips when long videos need short-form cuts quickly.
Consequence: distribution speed can beat perfect editing.

→ GitHub Copilot → Windsurf
Use Windsurf when you want agentic coding across files, not autocomplete.
Consequence: engineering leverage rises, but review discipline matters more.

The shift is not about chasing every new tool.
It is about replacing slow workflows with sharper ones.
Pick one workflow this week.
Upgrade the tool.
Measure the time saved.

Which part of your stack is most overdue for replacement?

09/06/2026

Most people use AI at the surface and miss the system underneath.

Here is the simplest way to understand what AI is built on.

Think of AI like an iceberg.

Above the water:

→ ChatGPT
→ Claude
→ Midjourney
→ Gemini

That is what everyone sees.

But the useful part is underneath.

✅ Layer 1: Classical AI
This is the 1950s foundation.
Symbolic AI, expert systems, knowledge representation, logic, and reasoning.
Diagnostic check: if a workflow is rules-based, it may still live here.

✅ Layer 2: Machine Learning
This is where systems learn from data instead of fixed instructions.
Supervised learning, unsupervised learning, classification, regression, reinforcement learning.
Consequence: better data often beats a better prompt.

✅ Layer 3: Neural Networks
This is where connected nodes learn patterns.
Perceptrons, cost functions, activation functions, hidden layers, backpropagation.
Action: learn backpropagation once and modern AI becomes less mysterious.

✅ Layer 4: Deep Learning
This is where scale changed the game.
Transformers, CNNs, RNNs, LSTMs, autoencoders.
Before: narrow pattern recognition.
After: language, speech, images, and code became practical.

✅ Layer 5: Generative AI
This is the layer most leaders now recognize.
LLMs like GPT, Claude, and Gemini.
Diffusion models like Midjourney and DALL-E.
Multimodal models that combine text, image, audio, and video.

✅ Layer 6: Agentic AI
This is the 2026 shift.
Memory, planning, tool use, and autonomous ex*****on.
AI does not just answer.
It starts acting across workflows.

One simple leadership test:

→ Ask which layer each AI initiative depends on.
→ Ask what data, tools, and approvals it needs.
→ Ask where a human must stay accountable.

If nobody can answer those 3 questions, the project is probably tool-led instead of strategy-led.

The mistake is treating all AI as one thing.

It is not.

Different layers create different risks, skills, and opportunities.

If you lead a team, this matters.
You cannot build a strategy around a tool name.
You need to know the layer underneath it.

That is how you separate hype from useful adoption.

Which layer does your team understand least right now?

Adam Danyal

12/06/2026

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