Master AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design
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Build production-grade AI agents with modern architectures and patterns.
Invoke this skill when:
| Parameter | Type | Required | Description | Default |
|---|---|---|---|---|
task | string | Yes | What agent capability to build | - |
architecture | enum | No | single, multi, hybrid | single |
framework | enum | No | langchain, langgraph, custom | langgraph |
complexity | enum | No | basic, intermediate, advanced | intermediate |
# Basic ReAct Agent
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-sonnet-4-20250514")
agent = create_react_agent(llm, tools=[search, calculator])
result = await agent.ainvoke({"messages": [("user", "What is 25 * 4?")]})
# Thought → Action → Observation loop
graph = StateGraph(AgentState)
graph.add_node("think", reason_node)
graph.add_node("act", action_node)
graph.add_node("observe", observation_node)
# Create plan → Execute steps → Verify
planner = create_planner(llm)
executor = create_executor(llm, tools)
# Execute → Reflect → Improve
agent_with_reflection = add_reflection_layer(base_agent)
| Issue | Solution |
|---|---|
| Agent loops forever | Add max_iterations limit |
| Wrong tool selected | Improve tool descriptions |
| Context too large | Implement summarization |
| Slow responses | Use streaming |
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[00] TnR ABSOLUTE RIGOR — Systemic Verification Doctrine. Forced cognitive exertion for high-stakes investigations. Mandates absolute deconstruction, mathematical proof chains, and ≥95% derived certainty. Zero-tolerance for heuristics or sentiment-based reasoning. Shortcuts are defined as intentional hallucinations.
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Configures 2-Agent workflow between PM and implementation roles. Use when user mentions 2-Agent, 2エージェント, PM連携設定, Cursor設定, Cursor連携, 2-agent運用. Do NOT load for: 単独運用, ワークフロー実行, ハンドオフ処理.
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Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.
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Add a new AI coding agent to Agent Sandbox. Creates all required files (Dockerfile, templates, CI, docs) and wires the agent into the CLI, proxy, and build system.
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Add a new property to the AI agents database. Use when the user wants to add, create, or introduce a new column, property, field, or feature to track across all agents in the comparison matrix. Handles all four required steps - database updates, groups.json, table display, and GitHub issue templates.
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Add new Agent templates to the meta-agent-skills framework.
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