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Ramon

@ramon.here

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Building AI calling agents and automations
So every call and inquiry gets answered

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This is my favorite way to build an AI GTM stack:

Pick ONE recurring job.

Example:

"Turn every sales call into follow-up + CRM update + content ideas."

Then build around it:

→ Otter captures the conversation.
→ Claude extracts decisions, pain points & follow-ups.
→ HubSpot/Salesforce holds the customer context.
→ Notion stores reusable knowledge.
→ Zapier/n8n moves the information.
→ Canva/Gamma turns useful insights into assets.

What you'll get:
→ fewer manual handoffs
→ less lost context
→ faster follow-ups
→ more reusable customer intelligence
→ fewer tabs open just to finish one task

And here's the part most people miss:

MCP is exciting because AI can finally become useful between the tools.

That's where GTM workflows get weirdly powerful. by @ramon.here

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8 hours ago

CEO: "Can we deploy an AI agent for this?"

CTO: "Probably."

Legal: "What can it access?"

Operations: "Who owns the workflow?"

CFO: "What does it save?"

Security: "Can we stop it?"

…and suddenly the agent is no longer the interesting part.

This is why executive AI decisions need to happen BEFORE the build.

A useful agent needs:

a single workflow with clear ownership,
data you would actually trust,
authority boundaries you can explain in one sentence,
governance before production,
a real buy-vs-build decision,
and a measurable target.

One detail I especially like:

Write down what the agent is NEVER allowed to touch.

Most teams define what AI can do.

Far fewer define where it must stop.

That boundary becomes very important the first time the agent does exactly what you technically allowed it to do.
Save this before your next AI agent proposal lands on your desk.

Which one of these gets skipped most often at your company? Tell me in the comments.

Repost this for an executive about to approve an agent project without asking these questions. by @ramon.here

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2 days ago

Raise your hand if you've called a chatbot an "agent" in a pitch deck this year. Be honest.

Here's the full checklist. If more than 2 of these are true, wait before you build.

1. You confuse chatbots with agents.
If the system cannot plan, use tools, or complete multi-step work, it is not an agent yet.

2. You haven't documented the job.
Agents perform best when the task, inputs, outputs, and edge cases are clearly defined before automation begins.

3. You trust autonomy too early.
Before full delegation, prove the system can work safely with approvals, logs, and human review in the loop.

4. You skip system design.
Good agents are built on workflows, memory, permissions, and handoff logic, not just clever prompting.

5. You overlook exception handling.
The real test is what happens when the input is messy, incomplete, contradictory, or unexpected.

6. You don't know the ROI.
If the agent does not save time, reduce errors, or improve throughput, it may be complexity dressed as innovation.

7. You're building before learning the business.
Without business context, the agent may do tasks faster while still solving the wrong problem.

Hand still up? Start at # 1. That's your actual starting point, not a limitation.

5 out of 7. Score yourself. Then fix one before your next build.

Repost this for the person who's about to find out their score too.

Save this before your next agent review. by @ramon.here

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3 days ago

Here are the other 19 laws.

1․ Give it a role

Depth and tone shift on their own.

2․ Set guardrails

What it must never do matters as much as what you ask for.

3․ Use examples

Claude copies patterns faster than it follows instructions.

4․ Push back on the first draft

Never accept it.

5․ Stress test everything

Ask for 5 tough questions before anyone else finds the holes.

6․ Switch models for the task

Sonnet for everyday work, Opus for complex strategy.

7․ Upload your files

Stop re-explaining context every session.

8․ Save your best prompts

Reuse them. Improve them.

9․ Use projects

Add your rules once, Claude works from that context every time.

10․ Keep projects simple

One project, one task.
11․ Use Claude skills

Pre-built expert rules with zero setup.

12․ Use the right mode

Chat to think, Code to build, Cowork for repeat tasks.

13․ Use connectors

Notion, Slack, Gmail, all in one place.

14․ Turn on memory

No more starting from zero.

15․ Use artifacts

Ask for something interactive, straight from chat.
16․ Get options

Ask for 5 versions instead of fixing one weak draft.

17․ Use extended thinking

Better outputs on anything that matters.

18․ Use voice mode

Brain dump first, clean up later.

19․ Demand straight answers

If Claude cannot explain it to a 10 year old, the idea is not ready.

The prompt was never the weak part. Everything around it was.

P.S. Which law is missing from your setup? 👇 by @ramon.here

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4 days ago

This is my favorite way to build an AI team for a small business:

(Don't start with agents.)

Start with your org chart.

Write down the work happening every week:

→ competitor research
→ content creation
→ design
→ admin
→ reporting
→ customer support
→ planning

Now turn each repeated job into an AI role.

Example:

Research
Perplexity + ChatGPT + Claude

Content
ChatGPT + Claude + Copy.ai

Design
Midjourney + DALL·E + Canva

Operations
Zapier + Make + ChatGPT

Data
ChatGPT + Claude + Sheets

Support
ChatGPT + Intercom + your knowledge base

Strategy
Claude + ChatGPT + your business context

What you’ll get:

→ clearer responsibilities
→ better prompts
→ less tool confusion
→ repeatable workflows
→ fewer tasks falling between apps

And here’s the part most people miss:

The "agent" is not the tool.

The agent is the job + context + workflow + rules.

Once you understand that, building the team gets much simpler.

Save this before you plan your next quarter.

Which agent would save you the most time right now? Tell me in the comments.

Repost this for a founder still doing all 7 of these jobs alone. by @ramon.here

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4 days ago

Your AI agent has amnesia.

Here are the 5 memory layers that fix it.

Working Memory

↳ In-context state, bounded by the context window. Resets at session end. Lives in the prefrontal cortex equivalent, focus and decisions for the moment.

Episodic Memory

↳ A cross-session record of what happened. Append-only, retrieved at planning time. Lives in the hippocampus equivalent, experiences and timelines.

Semantic Memory

↳ A typed entity store built by extraction. Dedupes and resolves contradictions. Lives in the temporal lobe equivalent, knowledge and meaning.

Procedural Memory

↳ A versioned skill registry built through reflection passes over past episodes. Prevents repeated mistakes. Lives in the basal ganglia and cerebellum equivalent, habits and skills.

Meta-Memory

↳ The offline control plane. Archives, dedupes, deprecates, decays. Keeps the whole stack healthy. Lives in the anterior cingulate cortex equivalent, monitoring and control.

The runtime pipeline: user input goes into working memory, the LLM reads episodic, semantic, and procedural memory at planning time, executes tools, responds, then writes back to memory after the turn.

This is not just prompt engineering. It is architecture.

Save this before your next agent build.

Which memory layer is missing from your current agent? Tell me in the comments.

Repost this for someone building an agent that forgets everything after one session. by @ramon.here

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5 days ago

Count the tools mentioned across these 9 steps.

There are 20+. Nobody needs all of them for a first build.

Here's what each step actually needs, and why.

1 → Define the role and use case. No tools yet. Just clarity: what problem, who benefits, what interactions. Example: a travel planner that searches flights, books hotels, builds an itinerary.

2 → Define inputs and outputs. Pydantic, JSON Schema, LangChain Structured Outputs. Schemas validate inputs, keep outputs consistent, and make the agent API-first instead of free-form text.

3 → Craft and improve prompts. GPT-4o, Claude, Llama Guard. Role-specific prompts first, tone and expertise guidance second, then test and refine.

4 → Add reasoning and external tools. LangChain, AutoGen, OpenAI Tools. ReAct-style frameworks combine reasoning with action, let the agent call APIs and databases, and enable chain-of-thought problem solving.

5 → Enable multi-agent collaboration. CrewAI, LangGraph, Swarm. Only needed once one agent isn't enough — Planner, Executor, Checker, coordinated.

6 → Handle memory and context. Pinecone, ChromaDB, Zep. Short-term vs. long-term memory, stored conversations and summaries, retrieved via embeddings.

7 → Add multimodal abilities (optional). Whisper, ElevenLabs, GPT-4 Vision. Only if voice or vision is actually part of the use case.

8 → Format and deliver outputs. Pandas, Markdown-to-PDF, Plotly. Human-readable and machine-friendly, both, not one or the other.

9 → Deploy with API or UI. FastAPI, Streamlit, Gradio. Expose it, integrate it, monitor it.

20+ tools. 9 steps. Most first builds only need steps 1-4 and step 9.

Repost this for the person about to install every tool on this list before writing a single prompt. by @ramon.here

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6 days ago

Nobody is going to hand you this AI agent roadmap.

So I'm doing it here. 📌

The AI agent landscape in 2026 is overwhelming.

→ New tools every week
→ New frameworks every month
→ Everyone claiming their stack is "the one"

I needed a single reference to organize everything.

This chart is it.

Left side → what we used to do (Chatbots, scripts, dashboards, manual glue)

Right side → what actually works now

Here's my cheat sheet for navigating it 👇

Start here if you're a beginner: → ChatGPT (model) + Zapier (orchestration) + Notion (knowledge)

Start here if you're intermediate: → Claude + n8n + MCP + Memory + Human Review

Start here if you're advanced: → Full 20-component stack with Multi-Agent Teams and Vector DB

Then add layers as you grow.

♻️ Repost this to help your network find their starting point.

P.S. Which level are you at, beginner, intermediate, or advanced? by @ramon.here

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7 days ago

AI is not ChatGPT.

Here are the 8 layers underneath it.

Data
↳ Raw facts, observations, and signals from the world. The foundation. Quality data drives everything.

Infrastructure
↳ Compute, storage, networks, and tools. The engine room. Without it, nothing runs.

Algorithms
↳ The logic and methods that find patterns in data. The recipe. Defines how learning happens.

Models
↳ Trained systems that learn patterns and make predictions. The brain. Learns patterns from data.

Machine Learning
↳ A subset of AI where models learn from data instead of rules. The approach. Learns from examples.

Deep Learning
↳ A subset of ML using neural networks with many layers. The horsepower. Handles complex patterns.

Generative AI
↳ A subset of DL that creates new content, text, images, audio, code. The creative layer.

ChatGPT and Tools
↳ Applications built on top of models to help you do things. The interface. Where users interact and act.

Common misconceptions this fixes:

→ AI is not ChatGPT, ChatGPT is one application.

→ AI does not work like magic, it works because of data, models, and compute.

→ More data does not always help, better data plus better models does.

→ AI does not replace people, it amplifies them.

→ AI is not the future, it is already here.

Next time someone says "AI is just ChatGPT," send them this.

Save this before your next AI conversation.

Which layer did you not know existed? Tell me in the comments.

Repost this for someone who still thinks AI and ChatGPT are the same thing. by @ramon.here

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8 days ago

AI model answers questions.

An agent gets work done.

The difference is 5 layers most people never see:

1. Agent Orchestration Layer, the brain of the agent

Handles planning, reasoning, tool selection, multi-step execution, human approvals, task coordination. Turns AI into a digital worker. Decides what to do, when to do it, with which tool.

2. Tool Layer (MCP), connects AI to enterprise systems

CRM, ERP, databases, APIs, ticketing. Allows the agent to take action. Connects to your systems and apps to get work done.

3. Memory Layer, provides context over time

User preferences, past conversations, long-running tasks, historical decisions, session context. Enables the agent to remember. Keeps context and state across interactions.

4. RAG, the knowledge layer

Policies, SOPs, product docs, knowledge bases, customer contracts. Gives the agent knowledge. Retrieves accurate, up-to-date information from your enterprise sources.

5. Foundation Model, the reasoning engine

GPT, Claude, Gemini, Llama. Provides reasoning and language understanding. On its own, it does not know your business, remember users, or take actions.

Enterprise agentic AI is all 5 layers working together. Foundation model plus RAG plus memory plus tools plus orchestration equals a system that actually gets work done, not just answers questions.

Save this before your next AI architecture conversation.

Which layer is missing from your current setup? Tell me in the comments.

Repost this for someone still calling a chatbot an "agent." by @ramon.here

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9 days ago

8 files turn a Claude Code folder into a real system. Nearly everyone just writes prompts and stops there.

I build AI automation and AI agents for small teams. On many of my client projects, the prompts were never the difference.

1․ CLAUDE.md, and keep it short

Overview, stack, commands, conventions. Nothing else. It loads at every session start, so every extra line costs you context all day.

2․ settings.json for permissions

Controls which tools your AI assistant can touch. Keep your own tweaks in settings.local.json.

3․ rules/ split by topic

Code style in one file, testing in another, API rules in a third. Each points at a specific path, so it loads only where it matters.

4․ commands/ for any workflow you repeat

Custom slash commands. They run shell too. Write the workflow once, run it forever.

5․ skills/ for the long instructions

Skills load only when the task matches. Move the big instruction blocks out of CLAUDE.md.

6․ mcp.json, one connection at a time

How Claude reaches your other tools: Slack, your CRM, your database. Every MCP adds weight to your context, so keep only the ones you use weekly.

7․ agents/ when one job gets big

Sub-agents get their own context window and their own tools. This is where it turns agentic. Your main session stays clean.

8․ hooks/ for the safety net

Small scripts that run before and after a tool. They lint, they check, they block a bad command.

Beginners tune prompts. Experts fix the folder.

P.S. Which of these 8 do you not have yet? 👇 by @ramon.here

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16 days ago

12 AI terms everyone uses.

Almost nobody can explain them properly.

I build AI agents and automations. Voice agents. Multi-agent systems. Automated hiring pipelines.

And I keep seeing the same thing:

People throw around words like RAG, tokens, and fine-tuning in meetings.

Then quietly Google them after.

You don't need a PhD to understand these.

You just need plain English.

So I broke down all 12 in the carousel below:

LLM. Hallucination. Token. Training vs. Inference. Fine-tuning. RLHF.
Distillation. RAG. Chain of Thought. Weights. Validation Loss. Coding Agent.

Each one explained the way I'd explain it to a client.

No jargon. No fluff.

Swipe through. Save it for later.

P.S. Which of these 12 did you pretend to understand at some point?

Be honest 👇

Your AI Partner 🤍 by @ramon.here

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a month ago