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Overview

The agent loop is the core execution cycle that runs for every incoming message. It follows a receive -> think -> act -> respond pattern, with multiple tool-use iterations possible before a final response.

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The Loop

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Step by Step

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1. Receive Message

The gateway delivers a normalized message to the agent runtime. The message includes the text content, sender identity, channel source, and any attachments.

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2. Context Injection

Before sending to the LLM, ArgentOS injects contextual information:
  • Timestamp envelope: [Wed 2026-02-12 10:30 America/Chicago] with elapsed time since last message
  • Memory bootstrap: Relevant memories recalled from MemU based on the message content
  • Plugin injections: Any active plugins can inject additional context

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3. Send to LLM

The assembled prompt (system prompt + conversation history + new message) is sent to the LLM via the model router. The router selects the appropriate model tier based on complexity scoring.

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4. Process Response

The LLM response can contain:
  • Text blocks: Natural language responses streamed to the user
  • Tool use blocks: Requests to execute tools (bash, memory, tasks, browser, etc.)

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5. Tool Execution

When the model requests a tool, the agent runtime:
1

Validate

Validates the tool call against the tool schema
2

Check policies

Checks tool policies (sandbox mode, approval requirements)
3

Execute

Executes the tool handler
4

Return result

Returns the tool result to the model
5

Continue loop

The model processes the result and may request more tools or generate a final response
This loop can iterate multiple times. A single user message might trigger several tool calls before the agent produces a final answer.

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Streaming

Responses are streamed token-by-token back to the user through the gateway. This means the user sees the response being generated in real-time, similar to the ChatGPT or Claude.ai experience. Tool execution happens between stream chunks. The user may see a pause while tools are running.

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Error Handling

If a tool execution fails, the error is returned to the model as a tool result with an error flag. The model can then decide to retry, try a different approach, or inform the user about the failure. If the LLM API call fails (rate limit, network error), the agent runtime uses the failover system to retry with a different provider or profile.