AGENTIC AI GLOSSARY · GUIDED LEARNING · REVIEWED SOURCES

Agentic AI Glossary

Understand agentic AI without fighting the vocabulary. Ask a question in everyday language, look up one of 721 reviewed concepts, or follow a guided path from foundations to governed multi-agent systems.

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721reviewed concepts
8 tracks32 guided lessons
Local MiniLMsame-origin proposals
Receiptsdeterministic replay
Filters & starting pointsCategory, entry type, lifecycle, and core concepts
Choose one clear starting point
Inspect how this answer was selected CATS route, Tier-S admission, candidate evidence, receipt, and replay
CATS route T0 · Exact identity
Query family General discovery
Executed query Browse all terms
Typed filters None
Why this path CATS is preparing the least expensive qualified path.
Active engine Deterministic fallback
T0Exactwaiting
T1Ready pathwaiting
T2Lexicalwaiting
T3Graphwaiting
T4Wasmwaiting
T5Cachewaiting
T6Semanticwaiting
T7Explainwaiting
Inspect ranking See the typed plan, corrections, field evidence, scores, and receipt
Original question
None
Normalized input
none
Executed query
browse all terms
Family
General discovery
Corrections
None
Alias expansion
None
Deterministic rewrite
None
Semantic coverage
4792 questions · 4026 alternate phrases
Filters
None
CATS route
T0 · Exact identity
Executor
Deterministic fallback while CATS prepares
Engine
Deterministic JavaScript
Eligible paths
T0
Withheld paths
T1–T7 until qualified
Cache
No sealed result reused
CATS configuration
waiting
Tier-S verification
Waiting to shadow-check the selected route.
Receipt
waiting
Top admitted candidatesStable score and visible evidence
    Product, UX & Learning

    Claude Code

    industry
    claude-code
    Entry type
    product
    Publisher
    Anthropic
    Lifecycle
    current
    Reviewed
    2026-08-14
    Delivery
    Terminal, IDE, desktop, browser, and SDK-backed product

    Why it matters

    It demonstrates how an agent can combine repository understanding, tools, and iterative execution inside a developer-controlled work environment.

    Example

    A team evaluating Claude Code can compare its delivery model, lifecycle, tools, memory, approvals, and evaluation surface with Claude Agent SDK before choosing an architecture.

    Common confusion

    Including Claude Code in this learning center does not mean OverLift uses, endorses, or treats every advertised capability as independently verified.

    How OverLift uses it

    The Agentic AI Glossary catalogs Claude Code as a source-reviewed ecosystem profile. OverLift may compare its role, lifecycle, and capabilities, but the product receives no authority over evidence, policy, execution, or glossary receipts.

    Official and reviewed sources

    Explore the technical detail Formal definition, lifecycle, use boundaries, capabilities, prerequisites, and comparisons

    Technical definition

    Claude Code is an execution-oriented coding agent with project context, shell and file tools, permissions, subagents, MCP integrations, hooks, and programmable access through the Claude Agent SDK.

    Where it appears

    Agentic AI Glossary · Ecosystem Agentic AI Glossary · Compare Kernel proof

    Lifecycle and review boundary

    Current public product or framework; recheck official release notes before major adoption decisions.

    Use it when

    Use it when you want an interactive coding agent to investigate a repository, implement changes, run validation, and explain the work for human review.

    Avoid or reconsider when

    Do not grant broad tool access without reviewing repository instructions, secrets exposure, command permissions, generated changes, and test evidence.

    Capabilities named by official sources

    repository analysis file editing command execution MCP tools subagents hooks

    Common confusions

    Claude Agent SDK OpenAI Codex GitHub Copilot Cloud Agent

    Learn these first

    Compare with

    See this architecture working

    Move from one definition into Sean Findley’s product and engineering proof.

    LEARN THE SECURITY BOUNDARY

    Retrieval can inform an agent. Retrieved content cannot grant it authority.

    The glossary demonstrates the same OverLift rule it teaches: exact and semantic retrieval may propose useful evidence, but source eligibility, permissions, capability admission, deterministic fallback, Tier-S verification, receipts, and replay decide what the product may trust.

    PRIVACY What information leaves the chosen boundary?

    Prefer browser-local, same-origin, or offline processing; disclose only the approved evidence a remote service actually needs.

    SECURITY What can this code or agent reach, call, change, or persist?

    Use worker and WebAssembly containment, explicit imports, typed tools, deadlines, allowlists, and fail-closed defaults.

    AUTHORITY What is allowed to become official truth or state?

    Keep identity, permissions, policy, approval, exact execution, verification, receipts, and replay outside probabilistic control.

    Foundational research sources See foundational sources; every ecosystem profile also carries its own official source record

    OpenAI Agents SDK

    OpenAI

    Agents, tools, handoffs, guardrails, sessions, tracing, and human review.

    Model Context Protocol

    MCP Project

    Hosts, clients, servers, tools, resources, prompts, elicitation, and tasks.

    Agent2Agent Protocol

    A2A Project

    Agent Cards, tasks, messages, parts, artifacts, streaming, and agent discovery.

    Agentic retrieval

    Microsoft

    Knowledge bases, knowledge sources, query planning, subqueries, and citations.

    LangGraph persistence

    LangChain

    Threads, checkpoints, durable execution, interrupts, and time-travel debugging.