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agentic pipeline
Joined October 2025
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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 retweeted
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)
- 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)
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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 retweeted
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 retweeted
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
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
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 retweeted
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 retweeted
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 retweeted
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 retweeted
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 retweeted
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 retweeted
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.
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.
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**LLM‑as‑a‑Judge** – Using a separate language model to grade agent outputs/trajectories against a rubric.
**Golden Set** – Small, human‑curated set of labeled examples defining “good” behavior used for evaluation.
**Human‑in‑the‑Loop (HITL)** – Pattern where critical
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series of logs forming the full trajectory of one task
**Metric** – Aggregated numeric measure of behavior or performance (latency, error rate, task success).
**Agent Quality** – Multi‑dimensional measure including Effectiveness, Efficiency, Robustness, and Safety \& Alignment.
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