Orchestrates the agent loop
AI Models & Assistants / Knowledge library
Agent Runtime
Executes model turns, tool calls, handoffs, pauses, retries, and final outputs
Why learn Agent Runtime?
It supplies consistent lifecycle behavior so agent logic does not have to rebuild orchestration for every task.
The problem it solves
It manages the changing control state of long, branching, and failure-prone agent executions.
Where it came from
An agent runtime advances a run through model and application events. It may stream results, enforce turn limits, suspend for approvals, resume saved state, and coordinate multiple agents or tools.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Define Agent Runtime and identify its place in an agent architecture.
- Choose a task where Agent Runtime improves an agent system.
- Diagnose one failure caused by misusing Agent Runtime.
- Interpret a trace or state transition involving Agent Runtime.
- Explain one operational tradeoff of Agent Runtime.
Documentation and sources
- OpenAI Agents SDK: Running agents (openai.github.io)
Connect the ideas
Enables
Explore next
Real World Example
Automated Inventory Reconciliation Agent
Retail logistics firms managing high-volume warehouse stock.
The business problem
Manual inventory reconciliation is slow, prone to human error, and expensive, often leading to stock discrepancies that delay order fulfillment.
How this topic solves it
An Agent Runtime implementation that automates the execution loop, handling model turns, tool calls to database APIs, and handoffs to human auditors when discrepancies exceed defined thresholds.
- Define an Agent with specific instructions for inventory verification and tools for database lookups.
- Configure a RunConfig to manage tool execution concurrency, ensuring database load remains within safe limits.
- Initialize the Runner to handle the agent loop, automatically processing model-emitted tool calls.
- Implement handoff logic to transition the conversation to a human auditor if the model cannot reconcile a stock count.
- Execute the run using Runner.run() to process the full lifecycle from input to final output.
The flow illustrates how the Runner manages the agent lifecycle, including tool execution and final output generation.
Generates turns and tool calls
Executes local function calls
Result returned to user
- Runner → invokes model → LLM
- LLM → emits tool calls → Tool Execution
- Tool Execution → returns tool results → Runner
- LLM → produces final text → Final Output
Implementation example
This code maps to the Automated Inventory Reconciliation Agent by setting up a controlled execution environment that prevents database overloading during automated stock checks.
from agents import Agent, Runner, RunConfig, ToolExecutionConfig
# Define agent with inventory tools
agent = Agent(name="InventoryAgent", tools=[check_stock, update_db])
# Configure runtime to limit concurrent DB calls
config = RunConfig(tool_execution=ToolExecutionConfig(max_function_tool_concurrency=2))
# Execute the loop
result = await Runner.run(agent, "Reconcile SKU-123", run_config=config)Expected business outcome
Reduced manual workload for inventory teams and faster, more accurate stock reconciliation through automated tool-driven verification.
Limitations and checks
The runtime loop may hit a MaxTurnsExceeded exception if the agent enters a recursive tool-calling cycle, which can be checked by monitoring for this specific exception type during execution.