peterindia.net
Knowledge Hub

Agentic Artificial Intelligence

A curated gateway to the tools, platforms, frameworks, and resources powering the era of autonomous AI agents.

Agentic AI Resources

33 curated topics
1
Agentic AI Browsers →
Browser-based agents that navigate, research, and act on the web.
2
AI Coding Agents →
Agents that write, refactor, and ship code with minimal oversight.
3
AI Agents for Kubernetes →
Automated Kubernetes management and operations with AI agents.
4
AI Agent Frameworks →
Frameworks and SDKs for orchestrating single and multi-agent systems.
5
Top Agentic AI Frameworks →
14 frameworks with GitHub stars, from LangGraph and CrewAI to AutoGen, LlamaIndex and OGX, plus guidance on choosing one.
6
Agentic AI Communication Protocols →
MCP, A2A, Agent Client Protocol, AG-UI and agent payment protocols, with specifications and SDKs.
7
AI Agent Swarm Frameworks →
Swarm and multi-agent orchestration frameworks with handoffs, teams and large-scale simulation.
8
No-Code Agent Platforms →
Build capable AI agents visually — no programming required.
9
GitHub Repos for Claude Code →
Curated repositories to learn and master Claude Code.
10
Agentic AI Observability Platforms →
Tools for tracing, monitoring, and debugging autonomous agent behavior in production.
11
AI Agent Building - Learning Resources →
GitHub Repositories for learning AI Agent Engineering
12
AI Model Routers →
Directing every prompt to the right model for cost, quality, and speed — OpenRouter, Cursor Router, Ramp Router, and more.
13
AI Agent Gateways →
AI agent gateways route, secure, govern, and observe traffic across LLM providers, MCP servers, and agent-to-agent (A2A) communication
14
AI Agent Meshes and Interoperability →
A2A, MCP and AGNTCY protocols, agent meshes, gateways and enterprise agent platforms that let agents work together across vendors and frameworks
15
AI Model Inference Engines →
Inference engines are for raw multi-user throughput, KV-cache reuse across long shared prefixes, peak FLOPS on data-center silicon, or the ability to run a 7B model on a laptop with no GPU at all.
16
Agentic AI Platforms for Life Sciences →
From commercial field engagement to real-world evidence and drug discovery, a new generation of life-sciences-native agentic AI is automating the workflows
17
Renowned Guardrails for Agentic AI →
Here's a technical map of the twelve guardrail functions every agentic system needs
18
The Significance of Guardrails for the Agentic AI Era →
Why autonomy raises the stakes for AI agents, with agent risk lists, standards, vendor guidance, guardrail tools and red-teaming resources.
19
Open-Source Benchmarks for AI Coding Agents →
SWE-bench put agentic coding evaluation on the map
20
AI Agent Governance →
Discover, observe, and govern autonomous agents in production — Arthur AI, Galileo, WitnessAI, and more.
21
AI Agent Security →
Runtime tools that catch malicious tool use, prompt injection, and rogue agent behavior before it executes.
22
AI Agent Databases →
The data layer for agents: PostgreSQL, document, in-memory and vector databases, plus pgEdge Starfleet's database branch per coding agent.
23
Knowledge Graphs & Graph Technologies →
Knowledge graphs, graph databases, semantic graphs, graph query languages, graph neural networks and GraphRAG — structured context for AI agents.
24
Ontologies →
OWL, RDFS, SKOS and SHACL standards, editors, reasoners and platforms for the semantic layer that gives AI agents shared, explainable meaning.
25
Agentic AI Platforms →
Curated directory of platforms for building, running and managing autonomous AI agents.
26
AgenticOps (AgentOps) →
Operating autonomous agents: the agent lifecycle, a staged-autonomy ladder, risks and tooling beyond LLMOps.
27
AI Agent Observability Overview →
How to monitor, trace and evaluate autonomous agents using MELT data: metrics, events, logs and traces.
28
Top MCP Servers →
Model Context Protocol servers that connect AI assistants and agents to tools such as GitHub, databases and design apps.
29
MCP Servers for AI Developers →
Ten MCP servers for AI coding agents: GitHub, Context7, Playwright, Chrome DevTools, Filesystem, Sentry, Brave Search, Fetch, Linear and Git.
30
Loop vs Graph Engineering →
Designing agent control flow: a self-directed agent loop versus an explicit graph of steps, with frameworks, papers and when to use each.
31
LLM-as-a-Judge Solutions →
Evaluation platforms, frameworks, judge models and bias research for scoring the outputs of LLMs and agents.
32
Causal LLMs →
Whether LLMs reason about cause and effect, with causal benchmarks, causal discovery, causal analysis agents and causal designs for agent planning, memory and safety.
33
Advanced Architectural Patterns for Agentic AI →
Six patterns for structuring agent systems (ReAct, sequential, router, supervisor, reflection and swarm), with context engineering, human-in-the-loop and evaluation.

Frequently Asked Questions

What is agentic AI?

Agentic AI describes systems built around AI agents that can pursue goals with some autonomy: they plan, call tools and data sources, take actions and adapt based on results, instead of only answering a single prompt. The guides in this hub cover the platforms, frameworks and controls needed to build and run them.

How is an AI agent different from a chatbot?

A chatbot mainly converses. An agent also acts: it can break a task into steps, use tools such as browsers, code environments and business systems through APIs or the Model Context Protocol, and carry the work through with limited supervision. Many products blend both.

What do I need to run AI agents in production?

Beyond a model and a framework, production agents need observability to trace what they do, a gateway or router to control model and tool traffic, guardrails, governance and security controls, a data layer for memory and retrieval, and an operations practice often called AgenticOps.

What is the Model Context Protocol (MCP)?

MCP is an open protocol that lets AI assistants and agents connect to external tools and data sources through standard servers. The Top MCP Servers guide lists widely used servers for services such as GitHub, databases and design tools.

Where should I start in this hub?

If you are choosing tools, start with AI Agent Frameworks, Agentic AI Platforms and No-Code Agent Platforms. If you are moving to production, read AgenticOps, AI Agent Observability, AI Agent Gateways, Guardrails and AI Agent Governance and Security.