AI product manager

Sharing practical tips and the raw truth while navigating the intersection of AI , business , and UX .

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[AI product manager @SaDoNoteifier]

From today i am going to share the knowledge on how to build an AI system :

Conceptual prerequisites the readers should understand by the end of the series:

1) What an AI agent is, and how it’s different from a simple chatbot.

[AI product manager @SaDoNoteifier]

*cheat.

[AI product manager @SaDoNoteifier]

chest code to understand agentic AI codebase ?

- learn parsing and processing difference
- learn the workflow between parsing and processing
- learn about the python data structures (bcz it is very helpful to understand shared state between agents)

[AI product manager @SaDoNoteifier]

chest code to understand agentic AI codebase ?

- learn parsing and processing difference
- learn the workflow between parsing and processing
- learn about the python data structures (bcz it is very helpful to understand shared state between agents)

[AI product manager @SaDoNoteifier]

- learn about the different messages works in an agent (bcz you have to know which data to be shared between the agent to prevent context rot)

[AI product manager @SaDoNoteifier]

2/n
- python function used as tool with speicific name and description
- the tool may do just parsing of skill md file
- skill md should contain name and description to easily scan to avoid latency

[AI product manager @SaDoNoteifier]

rules of thumb for building agentic AI 1/n

- scratchpad is specific to that agent , mostly contains ReAct messages.
- scratchpad final information is transferred to the state for state management
- that final info is what is needed for the future agents

[AI product manager @SaDoNoteifier]

2/n
- python function used as tool with speicific name and description
- the tool may do just parsing of skill md file
- skill md should contain name and description to easily scan to avoid latency

[AI product manager @SaDoNoteifier]

rules of thumb for building agentic AI 1/n

- scratchpad is specific to that agent , mostly contains ReAct messages.
- scratchpad final information is transferred to the state for state management
- that final info is what is needed for the future agents

[AI product manager @SaDoNoteifier]

rules of thumb for building agentic AI 1/n

- scratchpad is specific to that agent , mostly contains ReAct messages.
- scratchpad final information is transferred to the state for state management
- that final info is what is needed for the future agents

[AI product manager @SaDoNoteifier]

today i will post about- Building Agent Tools: APIs & Resiliency

1) Concept: Why Tools Matter
Foundation models are pattern‑predictors; they cannot see live data or take real actions without tools.
Tools are the bridge from static training to live systems: they let the agent

[AI product manager @SaDoNoteifier]

if you take this series as a base to your agentic ai curiosity, no one stops you in that journey

lets talk about database memory and chat interface today:

1) Concept: Sessions vs Long‑Term Memory
A session is a chronological log of one continuous conversation: events + state.

[AI product manager @SaDoNoteifier]

hi guys, hope your are enjoying the agentic ai series.

today in day4, let me share about importance of context engineering.

1) Concept: Context Engineering
Context engineering is how you dynamically build the model’s input each turn: system instructions, tools, history, RAG

[AI product manager @SaDoNoteifier]

in day3 lets talk about wiring Your Agent Pipeline using langgraph framework

1) Concept: Graph‑based Orchestration
You use a StateGraph (LangGraph) to describe nodes (agents/steps) and edges (transitions) forming a DAG (Directed Acyclic Graph).

[AI product manager @SaDoNoteifier]

After creating the first AI agent in ReAct pattern in day1,
Let's talk about the Multi‑Agent Architecture & Role Separation in Day2

1) Concept: Core Agent Architecture
Every agent you build follows three components:
Model (Brain): Does the reasoning and planning.

[AI product manager @SaDoNoteifier]

let me start with DAY 1:

1) Concept : What is an AI Agent?
An AI agent is a system built around a language model that can plan and take actions with tools to achieve a goal without constant human direction.

[AI product manager @SaDoNoteifier]

@svpino you haven't tried right agents.

[AI product manager @SaDoNoteifier]

@she00574 @m_hardhiklearns

[AI product manager @SaDoNoteifier]

Hope you are learning along with me -lets discuss the final part of glossary series :

**Agent2Agent (A2A) Protocol** – Standard for agent‑to‑agent communication across services, languages, and organizations

**Agent Card** – JSON descriptor of an agent’s identity, capabilities,

[AI product manager @SaDoNoteifier]

in previous thread , we saw how to build production grade agentic ai system

Now in overall , we will see the part 1 of glossary :

**Agent** – LLM‑based system that can plan and use tools to achieve goals autonomously.

**Agentic Problem‑Solving** – Loop of Think → Act →

[AI product manager @SaDoNoteifier]

decisions are paused for human approval or correction

**Evaluation‑Gated Deployment** – Only deploying agent versions that pass predefined evaluation suites and safety checks.

**Vertex AI Agent Engine** – Managed Google Cloud service for hosting, scaling, and observing agents.