Agentic.AI
Complete guide to autonomous agents—fundamentals, architecture, frameworks, applications, and challenges.
Continue on the dedicated site below—this portal keeps enterprise reference content alongside that curriculum.
Open Agentic.AI →00Introduction
Overview
Agentic AI systems pursue goals autonomously through perception, planning, tool use, and learning from feedback—operating with varying human oversight levels.
Technical Deep Dive
Differs from chatbots: agents maintain state, take external actions, and run multi-step workflows. Spectrum: copilot (human drives) → autonomous agent (human supervises) → fully autonomous (rare in enterprise).
Core loop: Observe → Orient → Decide → Act (OODA). LLM provides reasoning; tools provide grounding.
Practical Use Case
Operations team deploys inventory agent reconciling ERP vs. warehouse scanners nightly—auto-creates purchase orders under threshold, escalates discrepancies >$10k.
01Agent Architecture
Overview
The canonical agent loop: perceive state → plan → act via tools → reflect → update memory until the goal is met, a budget is exhausted, or a human takes over.
Technical Deep Dive
Layers: interface, orchestrator (graph/runtime), LLM reasoning core, tool plane (HTTP/DB/code), memory stores, and policy guardrails. Each tool invocation is audited with correlation IDs.
Anti-pattern: one mega-prompt with 20 tools and no explicit state—failures become non-reproducible.
Practical Use Case
Incident agent: read-only diagnostics automated; service restarts gated behind human approval in LangGraph.
Planning
Overview
Planning decomposes high-level goals into ordered subtasks with dependencies, resource estimates, and success criteria—before execution begins or dynamically during execution.
Technical Deep Dive
Approaches: upfront plan (CoT listing steps), interleaved (ReAct), hierarchical (HTN), LLM-as-planner + critic. Plans should be revisable when tool observations contradict assumptions.
Store plan in agent state for observability; compare planned vs. actual steps in eval.
Practical Use Case
Event-planning agent: plan lists venue → catering → invites with dates; weather tool failure triggers replan switching to indoor venue automatically.
Memory
Overview
Agent memory spans working (current thread), episodic (past sessions), semantic (facts), and procedural (skills)—each requiring different storage, TTL, and privacy controls.
Technical Deep Dive
Working: message list + summarization. Episodic: transcript store. Semantic: vector DB of user/org facts. Procedural: successful trajectories as few-shot.
Memory poisoning attack: false fact injected via user message—mitigate with write approval and source attribution.
Practical Use Case
B2B sales agent remembers client's procurement cycle (Q4 budget flush), preferred communication channel, past objection handling—renewal rate +12%.
Tool Usage
Overview
Tools extend agents beyond text generation into databases, APIs, code execution, and enterprise systems—under strict schemas and permission boundaries.
Technical Deep Dive
Each tool declares: name, description (for model routing), JSON Schema parameters, auth scope, timeout, idempotency class. Runtime validates before execution; never trust raw model output as SQL.
Sandbox code tools in ephemeral containers without egress to internal network except allowlisted hosts.
Practical Use Case
HR agent tools: get_employee_record (read), schedule_interview (write, approval required)—model cannot invoke write tool without policy token in context.
Governance & Safety
Overview
Governance defines who agents act on behalf of, what they may do, how decisions are audited, and when humans must approve—non-negotiable for enterprise deployment.
Technical Deep Dive
Controls: RBAC on tools, policy engine (OPA), immutable audit logs, kill switches, model allowlists, data classification tags on retrieval sources.
Align with EU AI Act risk tiers, NIST AI RMF, industry regs (HIPAA, PCI).
Practical Use Case
Banking agent cannot execute wire transfers >$5k without dual approval; all prompts/responses retained 7 years; quarterly bias audit on loan explanation texts.