AgenticWorks

A community for developers awakening to agentic AI. Hands-on lessons, enterprise-grade context engineering, and a forum that earns its quiet.

Platform

  • Learn
  • Forum
  • Showcase

Project

  • About

Community

  • Network
  • Code of conduct

Field reports

Monthly notes on what shipped, what broke, and what we learned.

© 2026 AgenticWorks. Built in public.

AgenticWorks
LearnShowcaseForumCommunity
Sign in
See it in action

Three rooms. One workshop.

Every tab below is a feature we already shipped or have wired scaffolding for. No vapor. No fluff. Browse before you sign up.

Track 1 · ML foundations

  1. 01The first neuronNow
  2. 02A pair of neurons
  3. 03A committee of three
  4. 04Backprop without tears
  5. 05Stacking the committee
  6. 06From neurons to transformers
lesson_01_first_neuron.ipynb
1import numpy as np
2 
3# A neuron is a voter. Three inputs, three weights, one bias.
4inputs  = np.array([0.7, 0.2, 0.9])
5weights = np.array([0.4, -0.3, 0.6])
6bias    = 0.1
7 
8z = np.dot(inputs, weights) + bias
9out = 1 / (1 + np.exp(-z))   # sigmoid: 'fire?'
10print(round(out, 3))         # -> 0.659
> 0.659 // the committee says: lean yes

Illustrated step

The neuron isn't a brain cell. It's a voter.

Three signals walk in. Each has a weight - how loud that voice gets. The neuron sums them, adds bias (its own opinion), and decides whether to fire. That's it. That's the whole story.

x1
0.7
x2
0.2
x3
0.9
->
fire

The awakening

Arise to your potential.

Three rooms in the workshop. Pick where you start — learn, discuss, connect. The community pulls the rest forward.

keep scrolling
Inside the workshop

How agents actually work in production.

The frameworks and disciplines behind agents that go from “works in a notebook” to “works under load.” Each primer goes deep on one; the ladder below shows how they fit together into a production stack.

Google

ADK

Production-grade agents on Google Cloud.

The Agent Development Kit Google ships Vertex agents on: async by default, model-agnostic, with first-class evaluation hooks. The primer walks from quickstart to a deployed multi-tool agent.

Built in public

A curated community building in the open.

Built for people breaking through the talking points and buzzwords and getting to actual collaboration alongside engineers who deploy agents in enterprise settings every week. No fluff, no hype loops, no influencer feed.

Founder note

“The internet has enough tutorials. It needs more workshops, places where you can sit next to someone who's already shipped what you're trying to build.”

AgenticWorks is the workshop I wanted when I first tried to put an agent into production and realized the gap between “works in a notebook” and “works under load with three other agents in the loop” was several disciplines wide. The mission is simple: grow a curated community of engineers who ship agents, then make it easy for everyone else to learn alongside them.

AgenticWorks Founder

What you'll find here

  • Lessons

    Illustrated tracks on ML → agents → production

  • Workshops

Read the primer
LangChain

LangGraph

Stateful, controllable agent loops.

Agents modeled as directed graphs with explicit checkpoints and replay. Stop hoping the LLM does the right thing; design loops you can debug, branch, and replay turn-by-turn.

Read the primer
CrewAI

CrewAI

Role-based multi-agent crews.

Build agents as a team of researcher, writer, and critic, then compose them into workflows that mirror how humans collaborate. The primer walks through a research-and-publish crew.

Read the primer
Microsoft

AutoGen

Multi-agent conversation framework.

Agents talking to agents. Strong for code-generation pipelines, adversarial evaluation, and any flow where two perspectives beat one. We use it as our reference for eval harnesses.

Read the primer

The discipline ladder

Six disciplines that turn a working prompt into a deployable agent. None of them are optional in production.

01Prompt engSTAGE02Context engSTAGE03Harness eng

Monthly live builds + archived recordings

  • Forum

    Threads tagged by stack, 48-hour answer SLA

  • Office hours

    Weekly rooms with senior practitioners

  • Pods

    Small open-source crews on focused 4-week sprints

    • STAGE04Context RAGSTAGE05OrchestrationSTAGE06Eval & obsSTAGE

    • Tighten the input. Constrain the output. Get repeatability before reaching for scale.

    • Manage the window. Summarize, compress, and route; long context is a budget, not a feature.

    • Wrap the model. Tools, structured outputs, retries, and guardrails turn an LLM into something callable from real code.

    • Retrieve the right truth. RAG isn't one pattern: PDFs, tabular data, and tables-on-PDFs each demand a different stack. Match the data shape with embeddings, rerankers, or hybrid.

    • Wire the loop. Planner → worker → critic. State machines beat free-form agent improv every time.

    • Trust by measurement. Golden traces, regression suites, and online dashboards: ship what you can prove.

    1. 01

      Prompt eng

      Tighten the input. Constrain the output. Get repeatability before reaching for scale.

    2. 02

      Context eng

      Manage the window. Summarize, compress, and route; long context is a budget, not a feature.

    3. 03

      Harness eng

      Wrap the model. Tools, structured outputs, retries, and guardrails turn an LLM into something callable from real code.

    4. 04

      Context RAG

      Retrieve the right truth. RAG isn't one pattern: PDFs, tabular data, and tables-on-PDFs each demand a different stack. Match the data shape with embeddings, rerankers, or hybrid.

    5. 05

      Orchestration

      Wire the loop. Planner → worker → critic. State machines beat free-form agent improv every time.

    06

    Eval & obs

    Trust by measurement. Golden traces, regression suites, and online dashboards: ship what you can prove.

    AI/ML community · Built in public

    Welcome to the AI/ML community thatshows you how Agentic Works.

    The workshop where engineers go from buzzwords to working agents alongside teams shipping at enterprise scale.

    Join the community
    See it in action
    AGENTICWORKFLOW
    FoundationsNeurons → LLMsANN · CNNs · Transformers
    Prompts & ContextEngineering the inputFew-shot · Routing · Compression
    Context RAGRetrieval patternsPDF · Tabular · Hybrid
    HarnessesProduction stacksTools · Eval · Guardrails
    ForumTagged threadsQ&A · Show & tell · Patterns
    WorkshopsLive build-alongsStreams · Archive · Drops
    MentorshipOffice hours1:1 · Reviews · Cohorts
    Open sourceBuild in publicRepo · Pods · MIT
    LEARNCOMMUNITY
    Lessons
    10 + growing
    Frameworks
    ADK · LangGraph · CrewAI
    Forum
    Curated for signal
    Cover image for "Episodic logs are not structured state."

    Memory in production

    Episodic logs are not structured state.

    Short-term scratchpads, long-term knowledge stores, and session boundaries each solve different problems. Treating chat history as memory is why your agent forgets what mattered.

    IMIngrid Martinez
    Read article
    Cover image for "Two research projects converged into one enterprise SDK."

    Microsoft stack

    Two research projects converged into one enterprise SDK.

    AutoGen taught Microsoft conversation loops; Semantic Kernel taught it enterprise plumbing. The Agent Framework is the merger, and the migration story matters more than the feature list.

    Mateo Lee
    Cover image for "The enterprise agent play is governance, not models."

    Conference watch

    The enterprise agent play is governance, not models.

    Cloud Next was less about a new model and more about where agents run, who governs them, and how they get discovered inside an org. Here's what Agentspace and the ADK push mean for enterprise buyers.

    Pavel Hernandez
    Cover image for "State machines beat agent improv."

    Orchestration patterns

    State machines beat agent improv.

    Letting agents riff produces great demos and terrible audits. Explicit planner–worker–critic loops encode rejection at the orchestration layer. The same move as scaling-your-no, but structural.

    Tessa Anderson

    Browse the rest

    Parsing is the product. Embeddings are the commodity.

    PDF war stories

    Hooks intercept. Skills package judgment.

    Core primitives

    Token counts don't explain tool failures.

    Production ops

    An agent isn't a chat loop with tools bolted on.

    Architecture basics

    Browse the forum