Guides · in review
AI agent guides
Reference guides on agent architecture, workflows, memory, and orchestration. These are reachable by direct link but stay noindex for now — see editorial methodology.
A pillar guide covers a broad topic end to end (what an AI agent is, at the level a newcomer needs). A cluster guide goes deep on one sub-topic the pillar references (agentic workflows, tool calling, multi-agent orchestration) and links back to the pillar for context.
Pillar
Clusters
Agentic Workflows — Patterns, Frameworks, and Failure Modes
Evidence-backed guide to agentic workflows: ReAct, plan-and-execute, branching, retries, HITL, and the engineering controls that keep workflows reliable.
AI Agent Autonomy Levels — A Four-Level Scale for Production Deployments
Evidence-backed four-level autonomy scale (L1 Assisted, L2 Supervised, L3 Delegated, L4 Autonomous) with HITL placement, audit requirements, and India-region considerations.
AI Agent Evaluation — How to Tell Whether an Agent Actually Works
Evidence-backed guide to agent evaluation: offline test sets, online evaluation, golden trajectories, LLM-as-judge, and what we score on this site.
AI Agent Memory — Working, Episodic, Semantic, and Procedural
Evidence-backed guide to agent memory: the four layers (working, episodic, semantic, procedural), retrieval, freshness, and the failure modes of memory without governance.
AI Agent Orchestration — The Engineering Discipline That Keeps Agents Reliable
Evidence-backed guide to agent orchestration: routing, retries, fallbacks, HITL, observability, and rollback. The difference between a demo and a production system.
AI Agent Tool Calling — The Primitive That Turns Models Into Agents
Evidence-backed guide to tool calling: how models emit structured payloads, how applications dispatch them, and the engineering controls that keep tool calls safe.
AI Agents for Startups — What Founders Should Buy, Skip, and Verify
Evidence-backed guide to AI agents for Indian and global startups: tools by use case, INR pricing with GST, DPDP considerations, and a verification checklist.
AI Agent Context Window — Management, Optimization, and Freshness
Evidence-backed guide to AI agent context windows: the four lifecycle stages (initial, growing, full, overflow), retention policies, DPDP compliance, and engineering controls that keep agents functional without ballooning cost.
AI Agent Prompt Versioning — Change Tracking Without Deployment Risk
Evidence-backed guide to prompt versioning: why it matters, how to version prompts, and the engineering controls that let teams iterate safely without breaking active deployments.
AI Agent Security Checklist — Pre-Deployment Review
Evidence-backed security checklist for AI agent deployments: permission scoping, tool validation, sandboxing, HITL placement, and audit logging. No agent should reach production without clearing every item.
AI Agents vs. Chatbots — What's Actually Different
Evidence-backed comparison of AI agents and chatbots: definitions, the tool-calling primitive that separates them, and a decision rule for which to use.
Human-in-the-Loop Agents — Where to Pause for Review
Evidence-backed guide to human-in-the-loop (HITL) patterns: which decisions require approval, where to put the checkpoint, and how to make HITL practical in production.
Multi-Agent Systems — When One Agent Isn't Enough
Evidence-backed guide to multi-agent systems: coordination patterns, role specialisation, conflict resolution, and the failure modes that make them harder than they look.