# Agentic Ai Projects And Mastery
All Agentic Ai Projects And Mastery notes →What is Gemini Enterprise Agent Platform
Google's April 2026 unification of agent tooling — a four-stage lifecycle (build, scale, govern, optimize) wrapping Agent Studio/ADK, a stateful Agent Runtime, an Identity/Registry/Gateway governance stack, and native A2A + MCP interop.
What is Google ADK
Google's open-source, code-first Agent Development Kit — a multi-language framework for building, evaluating, and deploying agents, positioned as an 'agent execution framework' rather than a toolkit.
What is CrewAI
Open-source Python framework for multi-agent orchestration — role-based 'Crews' for autonomous collaboration and event-driven 'Flows' for precise control, now a de facto standard for production agentic pipelines.
What is Hermes Agent
Nous Research's open-source, self-hosted AI agent that runs continuously on your own server, builds persistent memory over time, and distills its own reusable 'skills' from completed tasks.
What is OpenClaw
Self-hosted, model-agnostic personal AI agent (by Peter Steinberger) that gets full computer access — browser, filesystem, shell — and is reachable from 29+ chat platforms, positioned against SaaS agent walled gardens.
# Building Agentic Systems
All Building Agentic Systems notes →1. Evaluation Criteria
Covers the axes, including orchestration model, state management, observability, and ecosystem maturity, used to evaluate and compare agent frameworks before adopting one.
2. OpenAI Agents SDK
Covers OpenAI's Agents SDK primitives, agents, handoffs, guardrails, and sessions, and where it fits versus building an orchestration layer from scratch.
3. LangGraph
Covers LangGraph's graph-based state machine model for agent orchestration — nodes, edges, and conditional routing — and why that model, not a simple DAG, is what makes cycles, checkpointing, and human-in-the-loop interrupts first-class instead of bolted on.
4. CrewAI
CrewAI's role-based multi-agent orchestration model — crews, tasks, and processes — and where its opinionated defaults help you ship fast versus where they become a ceiling on custom control flow.
5. AutoGen
Covers Microsoft AutoGen's conversational multi-agent model — agents coordinate through group-chat message exchange and a speaker-selection policy instead of an explicit graph — and where that buys flexibility versus where it costs control.
6. Semantic Kernel
Covers Microsoft Semantic Kernel's plugin-and-planner model for embedding agentic behavior into existing enterprise .NET and Python applications, rather than building a new agent-first service from scratch.
7. Google ADK
Covers Google's Agent Development Kit — its workflow/dynamic-routing composition model, native tool-integration story, and the deployment path onto Vertex AI Agent Engine that is the actual site of vendor coupling, not the framework code itself.
8. LlamaIndex Workflows
Covers LlamaIndex's event-driven Workflows abstraction — steps wired by typed events instead of an explicit graph — and why the framework's RAG-first origins make it the natural home for retrieval-heavy agents.
9. Haystack Agents
Covers Haystack's pipeline-based approach to building agents on top of its retrieval and NLP component graph, aimed at production search and RAG use cases.
10. Choosing the Right Framework
Covers a decision framework for picking among the agent frameworks surveyed in this part, based on team skillset, orchestration complexity, and production observability needs.