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name: agentic-ai-workflows description: "Design and implement agentic AI systems that autonomously chain multiple tools to complete complex tasks. Covers tool composability, multi-step reasoning, approval flows, error recovery, and production deployment patterns." license: Apache 2.0 tags: ["agentic-ai", "tool-use", "composability", "multi-step", "claude", "mcp", "automation"] difficulty: advanced time_to_master: "10-20 weeks" version: "1.0.0"

Agentic AI Workflows

Overview

Agentic AI refers to systems where a language model autonomously plans, executes, and iterates on multi-step tasks using external tools. Unlike single-turn Q&A, agentic workflows involve tool chaining — where the output of one tool feeds the input of the next — enabling complex real-world automation.

When to Use This Skill

• Building AI agents that chain 3+ tool calls to complete a task
• Designing approval workflows for high-stakes AI actions
• Implementing error recovery and retry logic in tool chains
• Optimizing tool selection and routing for token efficiency
• Creating human-in-the-loop checkpoints for compliance
• Orchestrating multi-agent systems with specialized roles

Core Concepts

Tool Composability Architecture

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  Tool A      │────►│  Tool B      │────►│  Tool C      │
│  (Fetch Data)│     │  (Transform) │     │  (Take Action)│
└──────────────┘     └──────────────┘     └──────────────┘
       │                    │                     │
       ▼                    ▼                     ▼
  Context grows      Model reasons          Final result
  with results       about next step         delivered

Composability Patterns

Pattern	Description	Example
Sequential Chain	A → B → C in order	Fetch data → analyze → send report
Conditional Branch	If/else based on tool output	Check status → approve or escalate
Fan-out/Fan-in	Parallel tools, merge results	Query 3 APIs → combine insights
Iterative Loop	Repeat until condition met	Search → refine query → search again
Human Checkpoint	Pause for approval	Draft email → user approves → send

Claude's Composability Advantage

Claude naturally chains 4-5+ tools per turn when given "Always Allow" permissions. This is a decisive advantage over systems like ChatGPT that require per-action approval, creating "Permission Fatigue" that breaks complex workflows.

Implementation Guide

Designing Tool Chains

Rule 1: Each tool should return enough context for the model to decide the next step.

# Bad: returns only an ID
def get_account():
    return {"account_id": "acc_123"}

# Good: returns context the model needs for next decision
def get_account():
    return {
        "account_id": "acc_123",
        "balance": 15420.50,
        "status": "active",
        "last_transaction": "2026-03-28",
        "pending_invoices": 3
    }

Rule 2: Design tools as composable units, not monolithic endpoints.

# Composable tools for financial analysis
tools = [
    "get_account_summary",    # Returns balances, status
    "list_transactions",       # Returns recent transactions
    "run_forecast",           # Takes balance + transactions, returns projection
    "generate_report",        # Takes forecast, outputs formatted report
    "send_notification",      # Sends result to stakeholder
]

Human-in-the-Loop (HITL) Patterns

For high-stakes actions (payments, deletions, external communications):

server.tool(
  "request_approval",
  "Pause workflow and request human approval for a proposed action",
  {
    action: z.string().describe("Description of the action to approve"),
    details: z.object({
      type: z.enum(["payment", "deletion", "communication", "deployment"]),
      impact: z.string(),
      reversible: z.boolean(),
    }),
  },
  async ({ action, details }) => {
    const approval = await approvalQueue.submit({
      action,
      details,
      timestamp: new Date().toISOString(),
      status: "pending",
    });

    // Block until human responds
    const decision = await approvalQueue.waitForDecision(approval.id);

    return {
      content: [{
        type: "text",
        text: decision.approved
          ? `Approved by ${decision.approver}. Proceeding.`
          : `Rejected: ${decision.reason}. Workflow halted.`,
      }],
    };
  }
);

Error Recovery Strategies

┌─────────┐     ┌──────────┐     ┌───────────┐
│ Execute │────►│  Error?  │──No─►│  Continue  │
│  Tool   │     │          │      │  Chain     │
└─────────┘     └────┬─────┘      └───────────┘
                     │Yes
                     ▼
              ┌──────────────┐
              │  Retry with  │──Success──►  Continue
              │  backoff     │
              └──────┬───────┘
                     │Fail
                     ▼
              ┌──────────────┐
              │  Graceful    │
              │  degradation │
              └──────────────┘

Patterns:

1. Retry with exponential backoff for transient failures (network, rate limits)
2. Fallback tools — if primary API fails, try alternative data source
3. Partial results — return what you have with a note about missing data
4. Escalation — trigger human-in-the-loop when automated recovery fails

Context Window Management

As tools are chained, each result accumulates in the context window. Strategies:

• Summarize intermediate results — Don't pass raw API responses between steps; extract only what's needed
• Use tool-level pagination — Return 10 results with "more available" rather than 1000
• Implement "Code Mode" — Let the model write code that runs locally, keeping raw data out of context
• Clear completed context — Mark earlier tool results as disposable after they've been consumed

Multi-Agent Orchestration

┌─────────────┐
│ Coordinator  │ ◄─── User request
│   Agent      │
└──────┬───────┘
       │ Delegates
       ├──────────────────┬──────────────────┐
       ▼                  ▼                  ▼
┌─────────────┐   ┌─────────────┐   ┌─────────────┐
│  Research   │   │  Analysis   │   │  Execution  │
│   Agent     │   │   Agent     │   │   Agent     │
│  (read-only)│   │  (compute)  │   │  (actions)  │
└─────────────┘   └─────────────┘   └─────────────┘

Security Considerations

• Principle of least privilege — Each tool gets only the permissions it needs
• Input validation — Model-generated inputs can be adversarial; sanitize at every boundary
• Audit logging — Record every tool call, input, and output for compliance
• Rate limiting — Prevent runaway loops from consuming excessive API quotas
• Scope separation — Read-only tools should never have write access

Best Practices

1. Start simple — Build 2-tool chains before attempting 5-tool orchestrations
2. Test each tool independently before testing the chain
3. Design for idempotency — Tools may be called multiple times
4. Prefer structured outputs — Give the model clear data to reason about
5. Monitor token usage — Complex chains can consume 50K+ tokens per turn
6. Build kill switches — Every agent workflow must be interruptible

Resources

• Anthropic Tool Use Documentation
• MCP Specification
• Building Effective Agents (Anthropic)

Changelog

Version	Date	Changes
1.0.0	2026-03-31	Initial documentation

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