Use this skill when designing AI agent architectures, implementing tool use, building multi-agent systems, or creating agent memory. Triggers on AI agents, tool calling, agent loops, ReAct pattern, multi-agent orchestration, agent memory, planning strategies, agent evaluation, and any task requiring autonomous AI agent design.
Use this skill when designing AI agent architectures, implementing tool use, building multi-agent systems, or creating agent memory. Triggers on AI agents, tool calling, agent loops, ReAct pattern, multi-agent orchestration, agent memory, planning strategies, agent evaluation, and any task requiring autonomous AI agent design.
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AI Agent Design
AI agents are autonomous LLM-powered systems that perceive their environment,
decide on actions, execute tools, observe outcomes, and iterate toward a goal.
Effective agent design requires deliberate choices about the loop structure,
tool schemas, memory strategy, failure modes, and evaluation methodology.
When to use this skill
Trigger this skill when the user:
Designs or implements an agent loop (ReAct, plan-and-execute, reflection)
Defines tool schemas for LLM function-calling
Builds multi-agent systems with orchestration (sequential, parallel, hierarchical)
Applies planning strategies like chain-of-thought or task decomposition
Adds safety guardrails, max-iteration limits, or human-in-the-loop gates
Evaluates agent behavior, trajectory quality, or task success
Debugs an agent that loops, hallucinates tools, or gets stuck
Do NOT trigger this skill for:
Framework-specific agent APIs (use the Mastra or a2a-protocol skill instead)
Pure LLM prompt engineering with no tool use or autonomy involved
Key principles
Tools over knowledge - agents should act through tools, not hallucinate
facts. Every external lookup, write, or side effect belongs in a tool.
Constrain agent scope - give each agent a narrow, well-defined goal.
A focused agent with 3 tools outperforms a general agent with 20.
Plan-act-observe loop - structure the core loop as: generate a plan,
execute one action, observe the result, update the plan. Never batch
unobserved actions.
Fail gracefully with max iterations - every agent loop must have a hard
ceiling on steps. When the limit is hit, return a partial result with a
clear error message - never loop indefinitely.
Evaluate agent behavior not just output - measure trajectory quality
(tool selection accuracy, step efficiency), not only final answer correctness.
A correct answer reached via a broken path will fail in production.
Core concepts
Agent loop anatomy
User Input
|
v
[ Planner / Reasoner ] <---- working memory + observations
|
v
[ Action Selection ] ----> tool call OR final answer
|
v
[ Tool Execution ]
|
v
[ Observation ] ----> append to context, loop back
The loop terminates when: (a) the agent produces a final answer, (b) max
iterations is reached, or (c) an explicit stop condition triggers.
Tool schemas
Tools are the agent's interface to the world. Each tool needs:
A precise, action-oriented description (the LLM's primary signal)
A strict inputSchema (validated before execution)
An outputSchema (validated before returning to the agent)
Deterministic, idempotent behavior where possible
Planning strategies
Strategy
When to use
Characteristics
ReAct
Interactive tasks with frequent tool use
Interleaves reasoning and acting; recovers from errors
Chain-of-thought (CoT)
Complex reasoning before a single action
Produces a scratchpad; no intermediate observations
Plan-and-execute
Long-horizon tasks with predictable subtasks
Upfront decomposition; each step is an independent mini-agent
import { z } from'zod'// Input and output schemas are the contract between the LLM and your system.// Keep descriptions action-oriented and specific.const searchWebSchema = {
name: 'search_web',
description: 'Search the web for current information. Use for facts, news, or data not in training.',
inputSchema: z.object({
query: z.string().describe('Specific search query. Be precise - avoid vague terms.'),
maxResults: z.number().int().min(1).max(10).default(5).describe('Number of results to return'),
}),
outputSchema: z.object({
results: z.array(z.object({
title: z.string(),
url: z.string().url(),
snippet: z.string(),
})),
totalFound: z.number(),
}),
}
const writeFileSchema = {
name: 'write_file',
description: 'Write content to a file on disk. Overwrites if file exists.',
inputSchema: z.object({
path: z.string().describe('Absolute file path'),
content: z.string().describe('Full file content to write'),
encoding: z.enum(['utf-8', 'base64']).default('utf-8'),
}),
outputSchema: z.object({
success: z.boolean(),
bytesWritten: z.number(),
}),
}
For detailed implementations of sequential pipelines, parallel fan-out with synthesis, and hierarchical orchestration patterns, see references/orchestration-patterns.md.
5. Add guardrails and safety limits
interfaceGuardrailConfig {
maxIterations: numbermaxTokensPerStep: numberallowedToolNames: string[]
forbiddenPatterns: RegExp[]
timeoutMs: number
}
classGuardedAgentRunner {
privateconfig: GuardrailConfigprivate iterationCount = 0private startTime = Date.now()
constructor(config: GuardrailConfig) {
this.config = config
}
checkIterationLimit(): void {
if (++this.iterationCount > this.config.maxIterations) {
thrownewError(`Agent exceeded max iterations (${this.config.maxIterations})`)
}
}
checkTimeout(): void {
if (Date.now() - this.startTime > this.config.timeoutMs) {
thrownewError(`Agent timed out after ${this.config.timeoutMs}ms`)
}
}
validateToolCall(toolName: string, input: string): void {
if (!this.config.allowedToolNames.includes(toolName)) {
thrownewError(`Tool "${toolName}" is not in the allowed list`)
}
for (const pattern ofthis.config.forbiddenPatterns) {
if (pattern.test(input)) {
thrownewError(`Tool input matches forbidden pattern: ${pattern}`)
}
}
}
async runStep<T>(step: () =>Promise<T>): Promise<T> {
this.checkIterationLimit()
this.checkTimeout()
returnstep()
}
}
6. Implement planning with decomposition
For detailed plan-and-execute implementation with topological task ordering and dependency resolution, see references/orchestration-patterns.md.
One agent does everything; context explodes and tool selection degrades
Split into specialist agents with narrow charters
Unbounded loops
No maxIterations ceiling; agent hallucinates progress forever
Always set a hard iteration limit; return partial result on breach
Vague tool descriptions
LLM picks the wrong tool because descriptions overlap or are too general
Write action-oriented, specific descriptions; test with diverse prompts
Synchronous observation batching
Multiple tool calls before observing results; agent acts on stale state
Strictly interleave: one action, one observation, then re-plan
No input validation
Tool receives malformed input; crashes mid-run with cryptic errors
Validate with Zod (or equivalent) before executing; return structured errors
Evaluating only final output
Agent reached correct answer through a broken trajectory; won't generalize
Evaluate full traces: tool selection accuracy, redundant steps, error recovery
Gotchas
Missing maxIterations causes infinite loops - An agent with no ceiling on iterations will loop indefinitely when it gets confused, hallucinates a tool name, or enters a reasoning cycle. Always set a hard limit (10-20 for most tasks) and return a partial result with a clear message when it's hit. Never rely on the LLM deciding to stop.
Vague tool descriptions cause wrong tool selection - The tool description field is the primary signal the LLM uses to pick a tool. Descriptions that overlap ("get data" vs "fetch information") cause the agent to pick randomly. Write descriptions as action-oriented imperatives with specific use cases and clear exclusions.
Batching tool calls without observing breaks reasoning - Generating multiple tool calls before processing their results means the agent acts on stale state. The plan-act-observe loop must be strictly sequential: one action, one observation, re-plan. Parallel tool calls are only safe for truly independent queries.
Context window exhaustion mid-run - Long agent runs accumulate observation history that eventually exceeds the model's context window. Without a summarization or truncation strategy, the agent silently loses early context and starts making inconsistent decisions. Implement working memory summarization when history exceeds ~70% of the context budget.
Multi-agent trust boundaries - When an orchestrator delegates to worker agents, the worker's output is untrusted input to the orchestrator. An adversarial document processed by a worker agent can inject instructions into the orchestrator's context (prompt injection). Always sanitize worker outputs before incorporating them into the orchestrator's reasoning context.
References
For detailed content on agent patterns and architectures, read:
references/agent-patterns.md - ReAct, plan-and-execute, reflexion, LATS,
multi-agent debate - full catalog with design considerations
references/orchestration-patterns.md - Multi-agent orchestration (sequential, parallel, hierarchical) and plan-and-execute with task decomposition
Only load the reference file when the current task requires detailed pattern
selection or architectural comparison.
Companion check
On first activation of this skill in a conversation: check which companion skills are installed by running ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against the recommended_skills field in this file's frontmatter. For any that are missing, mention them once and offer to install:
