< agent lab />

Inside the control room.

This is how I think about agentic AI. Every system I ship follows a similar shape: an orchestrator brain, a typed set of tools, a memory layer, and a channel for the user to talk to it.

< generic agent architecture />

How an agent thinks

USER INPUT

text · image · voice

→

orchestrator

AGENT BRAIN

OpenAI Agents SDK · Gemini

→

OUTPUT

WhatsApp · Streamlit · API

tool

Web Search

Live web retrieval via SERP APIs

Tavily · SerpAPI

tool

RAG Knowledge Base

Cited answers from documents

LangChain · Pinecone

tool

Code Execution

Sandboxed Python for analysis

Python REPL

tool

External API Call

Connect to any HTTP service

n8n · Fetch

tool

Human Escalation

Route to a human when uncertain

WhatsApp · Slack

< interactive demo />

Try it yourself

Pick a scenario and watch a representative execution trace step by step. These demos mirror the architecture patterns used in the shipped projects below.

Input

"What is the attention mechanism in transformers?"

○

Embedding Query

○

Vector Search

○

Reranking Results

○

Generating Answer

< recorded traces />

Agent runs you can reason about.

Live demos are risky for public agent systems, so this page uses safe recorded traces and representative runs. Each trace highlights the decision points a recruiter or client should inspect.

safe demo mode

recorded