What is Claude Managed Agents?

Why Managed Agents? — Problems with Traditional Agents

Before understanding why Managed Agents are beneficial, you need to understand how difficult it is to build a traditional AI Agent — normally, if you want Claude to perform multi-step tasks (e.g., read a file, analyze it, write a report), you have to write your own Agent Loop:

Managed Agents solve all of these problems — Anthropic handles the Agent Loop, Sandbox, Tools, and Infrastructure entirely. You just send a single API Request and receive results via Streaming.

Claude Managed Agents — Key Facts

Topic	Details
Service Name	Claude Managed Agents
Developer	Anthropic
Launch Date	April 8, 2026 (Public Beta)
Type	Fully Managed Agent-as-a-Service (Serverless)
Supported Models	Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5
Built-in Tools	Code Execution, File I/O, Bash, Text Editor, Web Search
Streaming	Server-Sent Events (SSE) — real-time results
Sandbox	Isolated Container — code runs separately from the main system
Custom Tools	Supported — define your own Tools

Architecture — How Does It Work?

The core of Managed Agents is that Anthropic manages the entire Agent Loop on their own servers — here is a simple comparison with the traditional approach:

Traditional — You Manage the Loop

Your App -> Claude API (with tools) -> Claude responds "I want to use tool X"
Your App -> Runs tool X locally -> Sends result back to Claude -> Claude continues
(You must repeat this loop until Claude finishes)

New Approach — Managed Agents Handle It

Your App -> Agent API (single request) -> Anthropic runs the entire loop
  |-- Claude plans -> uses tools -> gets results -> plans next step -> ...
Your App <- Receives results via Streaming (SSE) step by step

Comparison — Managed Agents vs Traditional vs Competitors

Feature	Claude API (Traditional)	Claude Managed Agents	OpenAI Assistants API
Agent Loop	You manage it	Anthropic manages it	OpenAI manages it
Tool Execution	You run it	Runs on Sandbox	Runs on OpenAI
Streaming	Token-by-token	SSE + Tool Events	SSE
Custom Tools	Supported	Supported	Supported
Code Interpreter	None (DIY)	Built-in	Built-in
Web Search	DIY	Built-in	DIY
Security	Your responsibility	Isolated Container	Sandboxed
Cost	Predictable (1 call)	Variable (multiple iterations)	Variable
Best For	Simple Q&A	Complex multi-step tasks	Multi-step tasks

6 Key Features You Need to Know

Feature	Description
1. Autonomous Execution	Claude plans and executes multi-step tasks on its own without requiring approval at each step
2. Built-in Tools	Code Interpreter, File I/O, Bash, Text Editor, Web Search — ready to use immediately
3. Sandboxed Environment	All code runs in an Isolated Container separate from other systems, providing enterprise-grade security
4. SSE Streaming	View results in real-time via Server-Sent Events — see exactly what the Agent is doing at each moment
5. Custom Tools	Beyond built-in tools, you can define your own (e.g., pull data from an ERP system, send emails)
6. Token/Turn Limits	Set Token budgets and turn limits to control costs and prevent runaway Agent loops

Code Examples — Get Started in 5 Minutes

Below are working Python code examples, from basic to advanced.

Step 1: Install the SDK

pip install anthropic

Step 2: Create Your First Agent

import anthropic

client = anthropic.Anthropic()

# Create the simplest Agent — have Claude analyze data for you
agent = client.agents.create(
    model="claude-sonnet-4-6-20260410",
    name="data-analyst",
    instructions="You are a data analyst. Help analyze and summarize data in an easy-to-understand way.",
    tools=[
        {"type": "code_interpreter"},   # Can run Python code
        {"type": "file_io"},            # Can read/write files
    ]
)

# Command the Agent
result = client.agents.run(
    agent_id=agent.id,
    messages=[
        {
            "role": "user",
            "content": "Analyze sales from sales.csv, create trend charts, and produce a summary report"
        }
    ]
)

# The Agent will: read file -> write Python code -> create charts -> write summary
# All automated, no need to command each step individually
print(result.content)

Step 3: View Real-time Results with SSE Streaming

import anthropic

client = anthropic.Anthropic()

# Use Streaming to see what the Agent is doing in real-time
with client.agents.stream(
    agent_id=agent.id,
    messages=[
        {
            "role": "user",
            "content": "Create a monthly sales dashboard with 3 different chart types"
        }
    ]
) as stream:
    for event in stream:
        if event.type == "text_delta":
            # Text from Agent
            print(event.delta, end="", flush=True)
        elif event.type == "tool_use":
            # Agent is using a Tool
            print(f"\n[Using: {event.name}]")
        elif event.type == "tool_result":
            # Result from Tool
            print(f"[Done: {event.name}]")

Step 4: Add Custom Tools (Advanced)

import anthropic

client = anthropic.Anthropic()

# Define a Custom Tool — e.g., pull data from an ERP system
custom_tools = [
    {
        "type": "custom",
        "name": "get_sales_data",
        "description": "Pull sales data from the ERP system",
        "input_schema": {
            "type": "object",
            "properties": {
                "start_date": {
                    "type": "string",
                    "description": "Start date (YYYY-MM-DD)"
                },
                "end_date": {
                    "type": "string",
                    "description": "End date (YYYY-MM-DD)"
                }
            },
            "required": ["start_date", "end_date"]
        }
    }
]

agent = client.agents.create(
    model="claude-sonnet-4-6-20260410",
    name="erp-analyst",
    instructions="You are an ERP Analyst. Help analyze data from the ERP system.",
    tools=[
        {"type": "code_interpreter"},
        *custom_tools
    ]
)

# When the Agent needs to use a Custom Tool
# Anthropic will send an event back for you to run the Tool
# Then you send the result back to the Agent

Step 5: Set Token Budgets (Cost Control)

# Set max_tokens and max_turns to control costs
result = client.agents.run(
    agent_id=agent.id,
    messages=[
        {"role": "user", "content": "Analyze the last 3 months of data"}
    ],
    max_tokens=8192,    # Limit tokens per turn
    max_turns=10,       # Limit number of turns (Agent Loop iterations)
)

# If the limit is reached -> the Agent stops and returns whatever results it has

Pricing

Managed Agents charge based on actual Token usage (same as the regular Claude API), but since Agents work through multiple iterations, costs will be 10-50x higher than a single API call depending on task complexity.

Model	Input (per 1M Tokens)	Output (per 1M Tokens)	Best For
Claude Opus 4.6	$15	$75	Complex tasks requiring high accuracy
Claude Sonnet 4.6	$3	$15	Best balance of price and quality (recommended)
Claude Haiku 4.5	$0.80	$4	Simple tasks requiring speed

Use Cases — What Can You Do With It?

Use Case	What the Agent Does	Recommended Model
Data Analysis	Read CSV/Excel -> write Python code -> create charts -> write summary	Sonnet 4.6
Code + Testing	Design architecture -> write code -> run tests -> fix bugs -> deliver	Sonnet 4.6
Research + Report	Search the web -> read pages -> extract data -> write report	Sonnet 4.6
Document Processing	Read PDF/invoices -> extract data -> validate -> export Structured Data	Haiku 4.5
Security Audit	Scan code -> find vulnerabilities -> prioritize severity -> suggest fixes	Opus 4.6

Security Considerations

Managed Agents and ERP Systems — How Do They Connect?

For organizations already using an ERP system, Managed Agents can boost efficiency in several areas:

• Automated report analysis — pull data from the ERP, generate reports, and summarize key insights to help executives make faster decisions
• Cross-system data validation — compare accounting data with budget figures and flag anomalies
• Inventory document processing — read delivery notes, verify against POs, and record entries into the system
• IT Helpdesk support — automatically answer ERP usage questions from the user manual

Saeree ERP is currently developing an AI Assistant that will integrate with Agent technologies like this, enabling users to command the ERP system using natural language in the future. If you are interested, you can contact our advisory team for further discussion.

Summary — Who Is It For?

Good Fit	Not Yet Suitable
Developers who want to build AI Agents quickly	Tasks requiring real-time results within 1 second
Organizations that do not want to manage Infrastructure themselves	Tasks requiring access to Internal Systems without APIs
Data analysis and report generation tasks	Tasks requiring 100% accuracy (human review still needed)
Prototypes / MVPs that need Agents fast	Organizations with very limited cloud budgets (Agents use many tokens)
Systems requiring high Security (Sandbox)	Tasks where data cannot leave the organization (requires on-premise)

"We have moved from the era of AI answering questions to the era of AI doing work — Claude Managed Agents is the turning point that makes building AI Agents as easy as calling a regular API."

- Saeree ERP Team

References

• Anthropic Documentation — Agents
• Anthropic Unveils Managed Agents for Claude, Eyeing Enterprise AI Workflows
• Anthropic News — Official Blog

If your organization is interested in leveraging AI Agents to enhance productivity alongside your ERP system, you can schedule a demo or contact our advisory team for further discussion.