You will get Stabilize & Govern Your AI Agents (Production-Grade Fix in 48 Hours)
Project details
Most AI agents fail in production not because the model is weak, but because the system around it is unreliable. Hallucinations, broken tool calls, silent failures, and missing audit trails turn promising demos into operational risk.
This project focuses on AI Reliability Engineering — stabilizing and governing AI agents so they behave predictably under real-world conditions.
I work with teams whose agents already exist but are failing at scale. Instead of tweaking prompts, I diagnose system-level issues, fix broken orchestration, and implement guardrails, evaluation checks, and fail-safe controls. The result is an AI agent that knows when to act, when to escalate, and when not to respond at all.
Engagements are structured into clear tiers, from rapid failure diagnosis to fully governed, production-grade systems with audit-ready artifacts. Every project emphasizes measurable outcomes: reduced errors, controlled costs, and increased trust.
If your AI agent needs to move from experimental to dependable — this is built for you.
This project focuses on AI Reliability Engineering — stabilizing and governing AI agents so they behave predictably under real-world conditions.
I work with teams whose agents already exist but are failing at scale. Instead of tweaking prompts, I diagnose system-level issues, fix broken orchestration, and implement guardrails, evaluation checks, and fail-safe controls. The result is an AI agent that knows when to act, when to escalate, and when not to respond at all.
Engagements are structured into clear tiers, from rapid failure diagnosis to fully governed, production-grade systems with audit-ready artifacts. Every project emphasizes measurable outcomes: reduced errors, controlled costs, and increased trust.
If your AI agent needs to move from experimental to dependable — this is built for you.
AI Algorithms
Autoencoder, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, AIOps, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, PyTorch, StreamlitAI Models
BERT, BLOOM, ChatGPT, GPT-4, GPT-Neo, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,000
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | - | ||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | - | - | |
Model Monitoring | - | - | |
Model Testing & Optimization | |||
Model Tuning | - | ||
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Agent Scope
(+ 1 Day)
+$300Frequently asked questions
About Dan
AI Agent Engineer | LLM Systems, Orchestration & Reliability
Escondido, United States - 3:03 am local time
Many AI systems work in demos but fail at scale due to hallucinations, broken orchestration, silent errors, or missing auditability. My work focuses on fixing those system-level issues—moving AI from experimental to dependable.
I work with teams that already have AI agents in place and need them to behave predictably under real-world conditions. Rather than tweaking prompts, I diagnose failures across agent workflows, tool integrations, and decision logic, then implement guardrails, evaluation checks, and fail-safe controls.
My background spans AI agent development, LLM systems, automation, and production hardening. I’m experienced working across modern stacks (OpenAI, Azure, open-source models, APIs) and in environments where trust, compliance, and reliability matter as much as capability.
Engagements are structured, outcome-focused, and designed for speed—ranging from rapid failure diagnosis to fully governed, production-grade systems with measurable results.
If your AI agent needs to stop guessing and start behaving like a system you can trust, I can help.
Steps for completing your project
After purchasing the project, send requirements so Dan can start the project.
Delivery time starts when Dan receives requirements from you.
Dan works on your project following the steps below.
Revisions may occur after the delivery date.
Baseline & Failure Diagnosis
Review your agent architecture, trace execution paths, and identify failure modes, hallucination sources, and reliability gaps. Establish a clear baseline for “what’s broken and why.”
Stabilize Agent Workflows
Fix broken orchestration, tool calls, and decision logic. Add guardrails, structured outputs, and constraints to reduce hallucinations and silent failures.