# Debugging Agentic Loops: Common Failure Patterns

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# Debugging Agentic Loops: Where It Often Goes Wrong

 
 Published on community.llmnet.nl | Category: Agents & prompt engineering
 
 

 
 Building an LLM agent that carries out tasks on its own through an iterative execution loop looks manageable at first. A language model is given a goal, picks a tool, processes the result and decides on the next step. In a production environment, however, developers quickly discover that autonomous loops can behave unpredictably. A system that works correctly nine times out of ten locally can suddenly get stuck in infinite calls in production, or quietly pass on the wrong data.

 Debugging an agentic loop calls for a different approach than traditional software development. The logic is not captured in deterministic code alone; it emerges from the interplay between the instructions, the model quality, the context and the responses of external systems. Anyone moving from simple prompts to autonomous workflows — as described in the background piece on [AI agents explained](https://community.llmnet.nl/en/ai-agents-uitgelegd) — runs into specific failure patterns. In this article we cover the most common problems, how to recognize them, the underlying causes and concrete solutions.

 
## Failure pattern 1: Infinite repetition and repeating tool calls

 One of the most visible problems is an agent that keeps repeating exactly the same action. The model calls a tool with specific arguments, receives the result, but generates exactly the same tool call with identical parameters in the next iteration.

 
### Symptoms and detection

 In the logs you see a rapid sequence of identical API requests. The number of tokens processed grows linearly while the status of the task does not change. The application hangs until a global timeout kicks in or the API credits run out.

 
### Cause

 This behavior arises when the tool output contains no 'proof of progress' for the model. If the tool returns an empty response (such as {"status": "success", "results": []}), the model does not process this as a definitive signal that a search has been completed. The model assumes the action was not carried out properly and tries again. A second cause is that the model ends up in a deterministic 'local minima' of token probability when the temperature is set to 0 and the context does not change.

 
### Solution

 
 
- Adjust the tool response: Make sure tools state explicitly when an action produced no result, for example: {"status": "completed", "found_records": 0, "message": "Geen aanvullende gegevens beschikbaar."}.
 
- Repetition detection in the loop: Build a deterministic check into the execution loop. Compare the current tool call and arguments with the previous step. Is the call identical? Break off the loop and add a system message stating that this specific action has already been carried out without result.
 
- Refine the prompt structure: Use explicit [prompt patterns for agents](https://community.llmnet.nl/en/prompt-patronen-voor-agents) that state how the agent should act when a search returns zero results.
 

 
## Failure pattern 2: Getting stuck on a failed tool call

 An agent runs a tool call and the external system returns an error (for example an HTTP 500, a database timeout or an invalid API key). Instead of choosing an alternative route, the agent goes off the rails.

 
### Symptoms and detection

 The agent keeps repeating the failed call with exactly the same faulty parameters, or generates an unstructured text response in which the raw error message is shown to the end user as if the task had been completed.

 
### Cause

 Developers often pass the exception from their code straight to the model, or catch the error in the application layer without updating the model's context. As a result, the model has no clear instruction on how a technical error should be interpreted. The agent does not understand the difference between a functional error (such as "user not found") and an infrastructure error (such as "connection refused").

 
### Solution

 Handle errors in a structured way at the boundary between the application and the model:

 
 
- Catch the exception in your code and translate it into a clear JSON response for the agent.
 
- Distinguish between recoverable and non-recoverable errors. For temporary network problems the application should first use a pattern such as [retries and backoff](https://api.llmnet.nl/en/retries-en-backoff) at infrastructure level before the signal is passed to the agent.
 
- Send the model an explicit instruction when an error persists: {"error_type": "api_unavailable", "action_required": "Probeer een alternatieve bron of informeer de gebruiker dat deze gegevens momenteel niet ophaalbaar zijn."}.
 

 
## Failure pattern 3: Context saturation and the loss of earlier steps

 The more iterations an agentic loop runs, the more messages build up in the context. The history fills with prompts, tool calls, bulky JSON outputs and intermediate steps.

 
### Symptoms and detection

 In the later stages of the execution loop the model starts to ignore instructions from the system prompt. It forgets the original constraints, starts hallucinating or falls back on default behavior that conflicts with the assignment.

 
### Cause

 The phenomenon is known as 'context degradation' or the 'lost in the middle' effect. When the context limit is approached or exceeded, developers have to start pruning or summarizing messages. If the initial system prompt or crucial intermediate results are lost or pushed into the background in the process, the model loses its steering.

 
### Solution

 Managing the working-memory context is essential for complex applications. Extensive strategies for this can be found in the guide on [memory in LLM apps](https://leren.llmnet.nl/en/geheugen-in-llm-apps). The most important technical measures are:

 
 
- Context Pruning (trimming): Remove the bulky raw data outputs of old tool calls from the history once an interim conclusion has been drawn. Keep only the processed answer.
 
- System prompt anchoring: Make sure the general instructions and the stop criteria are always placed at the very beginning or the very end of the context and are never cut off in a sliding-window approach.
 
- Structured Architecture: For complex workflows with dozens of steps, move from a single free loop to a more structured setup, as described in the guide [from prompt to graph engineering](https://community.llmnet.nl/en/van-prompt-naar-graph-engineering).
 

 
## Failure pattern 4: Incorrectly parsed or invalid tool output

 The agent wants to call a tool, but the format generated by the model (such as JSON or XML) does not match the schema expected by the parser in the application.

 
### Symptoms and detection

 The application throws a JSONDecodeError or a validation error (such as Pydantic ValidationError). If this error is not handled properly, the application stops completely or the loop ends up in a crash loop.

 
### Cause

 Models sometimes generate text around a JSON block (for example Here is the JSON: ```json ... ```), forget to close brackets, or use the wrong data types (such as a string instead of an integer). This happens above all with more complex arguments, or when the model has to generate text and an action at the same time.

 
### Solution

 
 
 
 Method | 
 How it works | 
 Advantage | 
 

 
 
 
 Structured Outputs / Native Function Calling | 
 Use the model's API capability to strictly enforce the output via JSON Schema. | 
 Prevents parse errors almost entirely at the model level. | 
 

 
 Grammar-Based Decoding | 
 Restrict token generation on local models to valid syntax via BNF grammars. | 
 Guarantees 100% syntactic correctness. | 
 

 
 Self-Correction Loop | 
 Send the exact pydantic/JSON parsing error back to the model with a request to repair the JSON. | 
 Works well as a fallback with less strict models. | 
 

 
 

 
## Failure pattern 5: Stop criteria that are too vague

 The agent carries out the instructions but does not know when the task has been completed successfully. It keeps calling extra tools to "verify" things, or stays in a state of doubt.

 
### Symptoms and detection

 The final answer is already available in the context, but the agent still carries out 3 to 5 unnecessary steps before it gives a definitive answer. This significantly increases latency and operational costs.

 
### Cause

 The prompt contains instructions such as "Analyze the data thoroughly", but lacks an explicit definition of a 'done state'. The model has no clear separation between gathering information and delivering the final report.

 
### Solution

 Give the agent a specific final_answer tool or a strict stop token. Once the required information has been gathered, the instruction requires a call to final_answer(result=...) by the agent. When this tool is activated, the controlling script breaks off the loop immediately. Validate the quality of these stop criteria by carrying out structured [prompt testing for production](https://community.llmnet.nl/en/prompt-testen-voor-productie) in advance.

 
## Failure pattern 6: Silent failure propagation

 An intermediate step produces an incorrect or empty result, but the agent treats this as a valid outcome and builds on it. The end result is completely wrong, without the system having generated a single error.

 
### Symptoms and detection

 All status codes within the application show HTTP 200 and the agentic loop stops neatly. Only on manual inspection of the final report does it turn out that the facts are wrong or that crucial parts are missing.

 
### Cause

 Tools often return generic messages when data is missing. The agent interprets the absence of data as confirmation that a particular entity does not exist, or uses hallucinations to fill the gap.

 
### Solution

 Introduce validation steps in the software. Have the application check whether the output of a tool meets substantive quality criteria before it is added to the agent's context. If a tool is missing crucial data, the application has to mark this explicitly as a warning instead of as a neutral result.

 
## What you should log: the anatomy of a trace

 To debug an agentic loop after the fact, a standard text log file is not enough. Because an agent follows a dynamic path, you have to record every iteration as a structured 'trace'. For a full analysis of the observability of your systems, the article on [observability and logging](https://api.llmnet.nl/en/observability-en-logging) is worth consulting.

 For each iteration step in the loop, store at least the following fields in a structured format (such as JSON):

 
 
- Run ID & Step ID: Unique identification of the entire task and the specific sequence number within the loop.
 
- Full Prompt Input: The exact string/array of messages sent to the LLM, including the exact structure of the system prompt at that moment.
 
- Chosen Tool & Arguments: The tool the model selected and the parsed parameters.
 
- Raw Tool Output: The unmodified response the tool returned to the application.
 
- Parsed Tool Output: The cleaned or filtered information that was actually added to the model's context.
 
- Token Usage & Latency: The number of prompt tokens, completion tokens and the exact duration of the API call and the tool execution.
 
- Model Meta Data: Which specific model and version were used (for example via data from the overview of [models for agents](https://hub.llmnet.nl/en/modellen-voor-agents)).
 

 
## Reproducibility: replaying loops

 The most frustrating aspect of debugging agents is non-deterministic behavior: an error that occurs one time in ten. To fix an error effectively, you have to be able to replay the exact state of the system.

 
 
### Guidelines for deterministic testing:

 1. Fixed Seed: Where possible, use the seed parameter in the LLM API call. This offers no 100% guarantee with distributed inference, but it does increase reproducibility considerably.

 2. Transcript Replay (Mocking): Store the full sequence of messages and tool outputs. When debugging, you replace the actual tool executions with the previously stored responses (mocks). This isolates the LLM's decision-making from external variables such as network latency or changing database contents.

 

 
## Necessary guardrails: budgets and limits

 Do not ever let an agentic loop run without hard limits. Even a well-tested agent can end up in unforeseen situations where it keeps trying to solve a problem.

 As standard, implement the following three guardrails directly in the code of your execution loop:

 class AgentLoopController:
 def __init__(self, max_iterations=10, max_budget_usd=0.50, timeout_seconds=60):
 self.max_iterations = max_iterations
 self.max_budget_usd = max_budget_usd
 self.timeout_seconds = timeout_seconds
 self.current_iteration = 0
 self.accumulated_cost = 0.0
 self.start_time = time.time()

 def validate_continuation(self, step_cost):
 self.current_iteration += 1
 self.accumulated_cost += step_cost
 elapsed_time = time.time() - self.start_time

 if self.current_iteration > self.max_iterations:
 raise LoopAbortedError("Maximale iteratielimiet bereikt.")
 
 if self.accumulated_cost > self.max_budget_usd:
 raise LoopAbortedError("Financieel budget overschreden.")
 
 if elapsed_time > self.timeout_seconds:
 raise LoopAbortedError("Maximale uitvoeringstijd overschreden.")

 By writing these limits explicitly into the controlling software, you wrap the unpredictability of the language model in a deterministic safety layer. This prevents software errors or unexpected iterations from leading to sky-high API bills or stuck processes on the server.

 

 
 By Ivo Donker - compiled with AI assistance (Claude & Gemini) - Last updated: 2 August 2026