Fix Critical Bug in Custom Agentic AI Pipeline (LLM + Tools + Orchestration)

@irsolutions

With over a decade of experience in AI/ML development and high-security systems, I understand your need to fix a critical bug in your custom agentic AI pipeline. My background in scaling systems for over 1 million users and working on high-complexity FinTech projects directly applies to troubleshooting and optimizing your AI system. To ensure the success of your project, my strategic insight includes conducting a thorough analysis of the existing pipeline architecture and execution flow to pinpoint the root cause of the bug. Drawing from my past experience, such as successfully debugging and scaling Telegram Mini Apps, I am confident in my ability to identify and implement a robust fix for your system. I encourage you to take action and reach out to discuss the roadmap for fixing the critical bug in your AI pipeline. Let's collaborate to improve the stability and reliability of your agent loop and enhance the overall performance of your system.

@AITSoft

Dear Client, I understand you need a senior AI/ML engineer to debug and stabilize a custom agentic AI pipeline that combines LLM reasoning, tool calls, external APIs, and a multi-step orchestration flow. My approach is hands-on and iterative: I will first map the exact execution path (planner → executor → feedback loop) and instrument the code with focused, level-headed logging to pinpoint where state, tool-calling, or prompt handling diverges from expected behavior. I will then isolate the root cause, propose a clean, maintainable fix, and implement it with small, reviewable commits. After shipping the fix, I will validate stability across typical agent loops, add lightweight tracing for faster future debugging, and document the changes. If time permits, I can suggest architectural tweaks to improve fault tolerance and observability. The goal is a robust agent loop that behaves consistently under varied inputs and tool responses, with clear remediation steps for any future edge cases. 1) What is the current failure mode and its frequency across typical task types? 2) Are there any recent changes to prompts, tool schemas, or tool call limits that could trigger the bug? 3) Which parts of the state are critical for the planner and executor to agree on, and where does desynchronization occur? 4) Do you collect traces or logs for failed runs, and what minimum data should I preserve for debugging? 5) Are there preferred logging formats or observability hooks you want added? Best

@ShaikhAneesa

Hello, I have thoroughly reviewed the project requirements for fixing the critical bug in the custom agentic AI pipeline, which involves LLM-based reasoning, external tools/APIs integration, and multi-step orchestration. Let's chat and discuss it further. To handle your project, I will start with a detailed analysis of the existing pipeline architecture and execution flow. By leveraging my expertise in AI/ML, I will pinpoint the root cause of the bug, whether it pertains to logic, state handling, tool-calling, or prompt issues. Subsequently, I will implement a robust fix with clean, maintainable code, enhancing the stability and reliability of the agent loop while incorporating necessary debugging/logging for future reference. Before signing-off my bid, I would like to ask a question, i.e., how critical is the bug's impact on the overall system functionality? Best Regards, Aneesa.

@shadabkhan92

As an agency with a strong focus on agentic AI, my team and I are well-equipped to tackle the task at hand. We specialize in designing and implementing AI systems that operate seamlessly within existing workflows, making real-time decisions and producing tangible results. Our expertise lies in creating production-grade solutions rather than just prototypes, which is especially relevant to your project. What sets us apart is our diverse skill set and ability to work across multiple platforms. We have comprehensive knowledge and experience with LLM integrations, RAG pipelines, and predictive ML models - all relevant to the bug fixing job you need. Furthermore, we believe in addressing the root cause of a problem, not just its surface symptoms. So in line with your expectations, we will thoroughly analyze your existing architecture and execution flow to identify the exact issues causing inconsistent or incorrect agent behavior.

@kamran2012

Hello there, I will diagnose and fix the agent loop bug — tracing through your planner → executor → feedback flow to isolate whether the issue stems from state handling, tool-call parsing, or prompt drift across steps. I will also add structured logging at each orchestration stage. One common culprit: the LLM loses track of prior tool outputs when context windows fill up, causing hallucinated or repeated actions. Trimming and summarizing intermediate state often resolves this. Questions: 1) Which LLM are you using — OpenAI, Anthropic, or open-source? 2) Is the inconsistent behavior reproducible with a specific input, or intermittent? Looking forward to talking through the details. Kamran

@kareemmaize

Hi, the right way to fix this is to trace one failing run end to end instead of guessing whether the problem is prompt related, tool related, or orchestration related. A real flow here is: planner creates the next action -> executor calls a tool or LLM step -> result is written back into state -> feedback loop decides whether to continue, retry, or stop. If any one of those transitions is loose, the agent starts looking inconsistent even though each individual part seems fine on its own. These pipelines often look correct, but fail when tool outputs, intermediate state, and retry logic are not aligned across steps. One real issue is stale or partially written state causing the planner to act on the wrong context in the next loop, so I would handle it by tracing state transitions, validating tool responses before they reenter the loop, and tightening stop and retry conditions. The part to get right early is the state contract between planner, executor, and feedback loop, because that controls both reliability and correctness.

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Machine Learning (ML), LangChain, AI Chatbot Development, AI Model Development, AI Development, AI Agents and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@leciffre69

Hi dear , -Experience: https://www.freelancer.com/u/leciffre69 From my past experience, the real challenge is not fixing a single bug, but identifying hidden state or reasoning inconsistencies across the agent loop. This matters because issues in planner-executor-feedback cycles often stem from context loss, improper tool responses, or prompt drift. I’ve debugged similar pipelines where unstable outputs were caused by subtle state mismanagement and weak logging. To proceed, I only need access to your pipeline (repo or remote), sample failing cases, current logging setup, and which frameworks/tools are used (LangChain, custom, etc.). This is a straightforward project for me, and I’m sure in delivering a stable, reliable agent pipeline with proper fixes and improved debugging within 3 days. Let's chat now Thank you

@iglobalvn

I can quickly trace and fix the failure point in your agentic pipeline—whether it’s state drift, tool-call handling, orchestration logic, or prompt-induced instability. I’m a senior Python/AI engineer with hands-on experience building and debugging LangChain-based agents, multi-step tool-using workflows, and LLM orchestration systems. For this project, I’ll focus on the exact execution path causing inconsistent behavior and deliver a clean, durable fix rather than a patch. Key strengths I’d bring: - Deep debugging of agent loops, memory/state, and tool execution flow - Strong Python engineering for maintainable, production-safe fixes - Practical experience improving reliability in LLM + tools pipelines with logging and guardrails My approach: review the current architecture and logs, reproduce the bug, isolate the root cause, implement the fix, add targeted debugging output, and validate the agent loop across multiple scenarios. If there’s a structural weakness, I’ll also suggest a minimal architectural improvement to prevent recurrence. If you’d like, I can start by reviewing the current implementation and identify the likely failure point fast.

@vishal1145

Your agent loop is probably failing because of state corruption between tool calls or malformed function schemas causing the LLM to hallucinate invalid actions. I've debugged this exact pattern in 4 production agentic systems - usually it's either the orchestrator not resetting context properly or tool outputs not matching the schema the planner expects. Quick question - are you seeing the bug consistently with specific tool sequences, or is it random? And what's your current observability setup - are you logging the full prompt/response chain between planner and executor? Here's how I'll approach the fix: - LANGCHAIN AGENT LOOP: Trace the execution graph to find where state is leaking between iterations. I'll add structured logging at each node to capture tool inputs, LLM responses, and context windows. - TOOL CALLING SCHEMA: Validate that your function definitions match what the LLM is actually returning. Mismatched types or missing parameters cause silent failures that look like reasoning errors. - PROMPT ENGINEERING: Check if your system prompt is drifting during multi-step chains. I'll implement prompt versioning and add guardrails to prevent context overflow. - ERROR HANDLING: Add retry logic with exponential backoff for API calls and implement graceful degradation when tools fail instead of crashing the entire loop. I've fixed similar bugs in agentic systems for 3 clients where the issue turned out to be race conditions in async tool execution or the LLM getting stuck in reasoning loops. I don't take on debugging work without full access to logs and the ability to run the pipeline locally first. Let's do a 20-minute screen share so I can see the failure mode in real-time and confirm this is fixable within your current architecture before we start.

@alizain721

You mention a planner → executor → feedback loop and inconsistent agent behavior — that usually points to state propagation or tool-call routing breaking between steps. Often the visible symptom is a prompt or tool output getting stale or overwritten; the real problem is the execution flow losing the canonical state or mis-tagging tool responses. I recently fixed a LangChain agent where executor results were being replaced by concurrent planner updates; I added deterministic state envelopes and targeted logging which eliminated the incorrect actions. My plan: reproduce the bug with a minimal failing case, add structured tracing around planner, executor, and tool calls, identify where state or prompt context is lost, implement a small, maintainable fix (state locking, explicit response envelopes, or prompt fixes), add tests and logging, and deliver the working code via remote access. Can you share a failing trace or a repo link and tell me your preferred remote access method (SSH, Codespaces, or screen-share)? My bid is $20 and I can start right away.

@mugencodes

Hello, I understand the urgency of resolving the critical bug in your custom agentic AI pipeline integrating LLM-based reasoning, external tools/APIs, and multi-step orchestration. As a seasoned AI/ML engineer with expertise in Python, AI model development, AI chatbot development, and machine learning, I am confident in my ability to quickly identify the root cause of the issue and implement a robust fix. My approach will involve a thorough analysis of the existing pipeline architecture and execution flow to pinpoint the bug's source, whether it be related to logic, state handling, tool-calling, or prompt issues. I will then proceed to rectify the problem with clean, maintainable code, enhancing the stability and reliability of the agent loop while incorporating necessary debugging/logging mechanisms. Additionally, I can provide suggestions for architectural improvements to optimize the system further. I am committed to delivering a working fix implemented via remote access efficiently and effectively. Best regards, Jayabrata Bhaduri

@aidigitalhub

With a decade of experience in AI development and an expertise in machine learning, I am confident that I'm the right person to address the critical bug in your custom agentic AI pipeline. My skillset aligns perfectly with the scope of work you've outlined. Not only can I analyze complex pipeline architecture and execution flows swiftly, but I have a proven track record of quickly identifying the root causes of such bugs as well. The consistent theme of my work has always been reliability and stability. I deeply understand that in AI systems, a single bug can disrupt the entire essence of its functioning leading to incorrect output rendering it useless for your intended purpose. With this awareness, I always develop clean, maintainable codes that improve every aspect you mentioned from prompt issues to tool-calling to state handling. I will not only fix the existing issue but also enhance the agent loop significantly. What separates me from others is my understanding that technology is not static; it changes rapidly. Hence, being up-to-date is crucial. As a researcher, a qualified tech expert, and adept Python coder with excellent debugging techniques, I offer more than just fixing the issue at hand – ifold potential architectural improvements to make your pipeline even more efficient and future-proof. I'll make sure your system remains robust amidst industry’s underlying trends while providing maximum utility for your company's unique needs.

@techsty2014

Interesting project, Agent loops usually fail at one of four seams: tool-call schema drift with no validation to catch off-spec args, state leakage between planner and executor so replanning sees partial state and loops, prompt history accumulating past the context window so instructions drift out, or temperature above zero on the planner step introducing non-determinism. Fix pattern is structured logging at every hop (plan, tool-call, observation, re-plan) so a failing run can be diffed against expected state transitions, plus JSON validation on tool inputs and a hard iteration cap. Questions: 1) Pipeline on LangChain/LangGraph, AutoGen, or custom loop? 2) Bug on every run, or only on certain input patterns? Let's discuss via chat. Best regards, Faizan

@FizzaNadeemK

Stabilize your agentic pipeline fast by pinpointing the root cause and restoring consistent, reliable behavior. You’re running a planner to executor loop with tool-calling and feedback, so inconsistency usually comes from state leakage, prompt drift, or tool response handling, I’ve debugged similar systems where fixing memory flow and tool validation resolved erratic outputs. I’ll trace execution step by step, isolate the failure point, and implement a clean fix with stronger state control, deterministic tool calls, and structured logging for long-term stability. The result will be predictable agent behavior with clear observability so issues don’t resurface. I can work via remote access and patch directly within your existing architecture without disrupting the flow. Are you using any specific framework like LangChain, AutoGen, or a custom orchestration layer for the agent loop? Best Regards, Fizza Nadeem K

@aadips

Hi! Webneco Infotech has experience building agentic AI pipelines with LangChain, LLM tool-calling, and multi-step orchestration. We've debugged complex agent loops involving planner-executor-feedback patterns. We'll: 1. Analyze your pipeline architecture & execution flow 2. Identify root cause (logic, state handling, tool-calling, prompt issues) 3. Fix with clean, maintainable code 4. Test agent stability & document the fix We work with Python, LangChain, OpenAI/Anthropic APIs. Rate: $20/hr. Share your codebase and we'll start immediately!

@adriank06

Hi, ✓ Analyzing the full agentic pipeline including planner, executor, and feedback loop to trace inconsistent behaviors. ✓ Debugging tool-calling logic, prompt structure, and state transitions to identify the exact root cause. ✓ Fixing issues related to memory handling, execution flow, or API interactions with clean and maintainable code. ✓ Improving reliability of the agent loop by enforcing structured outputs and controlled decision boundaries. ✓ Adding detailed logging and tracing to monitor agent actions, tool usage, and intermediate states. ✓ Validating fixes through controlled test scenarios to ensure consistent and correct agent behavior. ✓ Optimizing orchestration flow to reduce failure points and improve response stability. ✓ Providing optional architectural recommendations for long-term robustness and scalability. ✓ Strong experience building and debugging agent-based AI systems with tool-calling and multi-step orchestration. ✓ Background in LLM integration, prompt engineering, and handling complex execution pipelines. ✓ Skilled in diagnosing state management issues and improving reliability in autonomous systems. ✓ Focus on clean fixes, observability, and stable production-ready AI workflows. Is the issue reproducible under specific inputs or intermittent across different execution scenarios?

@umekayani01

Hey there, Analyzed your agentic pipeline likely tool loop/state drift or hallucinated calls causing inconsistency. Fixed similar LangChain agents (reasoner-executor-feedback) fast. Deliver: Root cause ID, robust fix + logging/debug, stability boosts. Remote access. logic code: # Loop detect if len(tool_calls) > 5 and tool_calls[-5:][0]['tool'] == tool_calls[-1]['tool']: raise ValueError("Loop detected") Question: Pipeline code or repo access ready? Inbox me ASAP Umer Kayani

@ivicab6

With over 6 years of experience as a full-stack engineer, I have substantial exposure to building and maintaining complex software systems like the agentic AI pipeline you're working with. My proficiency in Python and Machine Learning will enable me to effectively analyze your current architecture and execution flow, detecting any underlying logic, state handling, tool-calling or prompt issues that could be causing the bug. Alongside resolving the immediate issue at hand, it would be my pleasure to suggest architectural improvements that could enhance the overall stability and reliability of your agent loop. Another aspect of my work that aligns with your needs is my dedication to producing clean, maintainable code with comprehensive logging and debugging capabilities- a crucial aspect for any AI system. I understand how valuable clear logs are for identifying and fixing issues, so I'll ensure solid debugging measures throughout the pipeline. Furthermore, not only can I mend the critical bug plaguing your system but I can also offer value additions by integrating my experience with data pipelines, analytics, and ML components. This means that if at all required or desired, I can bring a more holistic approach to your project by providing reporting, dashboarding and even forecasting capabilities for your AI solution. So let's work together to fortify your agentic AI pipeline quickly and robustly!