Implementing Loops for Multi-Step Agent Tasks
Real problems cannot be solved in a single prompt. Master the architecture of multi-turn execution loops: handling exit criteria, implementing repetitive-call circuit breakers, and preserving context across prolonged task execution.
1. Core Mechanics of the Agent Loop
Select a mechanism to inspectLoop Engine Implementation
# 1. THE MULTI-STEP WHILE LOOP ENGINE
def run_agent_loop(agent, user_query: str, max_turns: int = 6) -> str:
"""Executes Thought-Action-Observation loop until completion."""
agent.add_user_message(user_query)
for turn in range(1, max_turns + 1):
print(f"--- [Turn {turn}/{max_turns}] ---")
# 1. Model evaluates state and decides whether to act
step_response = agent.step()
# 2. Check termination: Has model produced a final answer?
if step_response.is_final:
return step_response.final_content
# 3. Dispatch tool execution
tool_name = step_response.action_name
tool_args = step_response.action_args
observation = agent.dispatch(tool_name, tool_args)
# 4. Append observation back into conversation context
agent.add_observation(tool_name, observation)
return "Error: Agent reached maximum iterations without reaching conclusion."2. Interactive Agent Loop Simulator
Live Multi-Turn State MachineMulti-Step Agentic Cycle Simulatorwhile Loop
Observe how an agent cycles through Thought ➔ Action ➔ Observation until an exit condition or iteration brake fires
"Find a hotel in Tokyo under $150 and calculate total cost for 3 nights with 10% tax."
for i in range(max_iterations):
thought, action = agent.step()
if not action:
return final_answer # EXIT!
obs = actions[action.name](action.args)
agent.add_observation(obs)Unlike a single-turn agent, a looping agent iterates until it decides no further tools are needed.
Common Engineering Traps
When a tool throws an exception (e.g. UserNotFoundError), poorly written loops return the raw error message to the LLM. If the model has no alternative tool, it re-attempts the exact same call 10 times until crashing. Always provide explicit error hints or prompt the model to request user clarification.
As loop iterations climb from 1 to 8, the accumulated conversation history grows quadratically in cost and latency. Without history compaction or sliding window trimming, later turns become 5x slower and increasingly prone to hallucination.
Key Architectural Takeaways
- 1.Deterministic Termination: Never rely on the LLM saying 'I am finished' in natural text. Rely on the structural absence of tool_calls in the model response object.
- 2.Circuit Breakers Save Budgets: A simple hash-based repeat detector prevents 99% of infinite loop billing disasters in autonomous production runs.
- 3.Graduation to Graph Paradigms: While-loops work well for single agents, but branching, conditional routing, and checkpoints quickly turn a raw while-loop into spaghetti code — which is why LangGraph was created.
Understanding Nodes and Edges in LangGraph
When while-loops grow too complex, graphs take over. Discover how LangGraph models agents as cyclical state graphs with discrete nodes, edges, and time-travel persistence.