You will get to build a Stateful AI Agent with LangGraph
Project details
Standard LLM implementations often hit a "wall" where they become unpredictable at scale. I solve this by building on a backbone of deterministic state machines and strict type-safety. Every project includes granular tracing via LangSmith and model-v-model evaluation in W&B Weave. You receive a fully auditable "Digital Employee" mapped to a comprehensive blueprint, ensuring your AI remains reliable as your workflows evolve
Machine Learning Tools
Azure Machine Learning, BERT, ChatGPT, Databricks Platform, GitHub Copilot, NLTK, NumPy, NVIDIA AI Platform, Open Neural Network Exchange, OpenCV, pandas, PyMC, Python, PyTorch, SciPy, Scrapy, TensorFlow, Tesseract OCR, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$450
|
Standard
$850
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 days |
Number of Revisions | 2 | 4 | 6 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 2 | 5 | 10 |
Number of Graphs/Charts | 1 | 3 | 5 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50
Data Source Connectivity
(+ 2 Days)
+$250
Monitoring Dashboard
(+ 3 Days)
+$300
Multi-Model Support
(+ 2 Days)
+$200
HITL Approval Layer
(+ 2 Days)
+$150Frequently asked questions
About Oreoluwa
AI Agent Architect | Multi-Agent Systems & LangGraph Specialist
Ibadan, Nigeria - 11:34 am local time
How I deliver value to your business:
✅ Build Autonomous Multi-Agent Workflows: I design sophisticated systems using LangGraph to treat agent logic as a directed graph. This enables complex decision cycles, error recovery, and "time-travel" debugging. ✅ Ensure Type-Safe Reliability: Using Pydantic AI, I implement durable execution and strict schema validation. Every response from your agent is guaranteed to be structured and validated before it hits your production database. ✅ Architect Framework-Agnostic Systems: I leverage the Agent-to-Agent (A2A) protocol and Model Context Protocol (MCP) to allow agents to securely discover tools and communicate across platforms, future-proofing your stack. ✅ Full-Cycle Production Engineering: From initial concept to containerized deployment, I bridge the gap between experimental scripts and live, scalable APIs.
Core Technology Stack:
Orchestration: LangGraph, AutoGen, CrewAI, Google ADK.
Logic & Safety: Pydantic AI, Pydantic (V2), Guardrails AI.
Protocols: A2A, MCP, REST, SSE (Server-Sent Events).
Infrastructure: Docker, FastAPI, AWS SageMaker, GCP Vertex AI.
Keywords: AI Agent Architect • Multi-Agent Systems (MAS) • LangGraph • Pydantic AI • Model Context Protocol (MCP) • Agent-to-Agent (A2A) • Agentic Workflows • Deterministic Autonomy • AI Automation • n8n Integration
Steps for completing your project
After purchasing the project, send requirements so Oreoluwa can start the project.
Delivery time starts when Oreoluwa receives requirements from you.
Oreoluwa works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Core Mandate & Knowledge Integration
Define organizational roles and security boundaries. Ingest proprietary data into secure vector stores using LlamaIndex to ensure grounded, factual retrieval and zero-hallucination outcomes
Step 2: Stateful Graph Architecture
Build the core engine via LangGraph. I model logic as a state machine with self-correction nodes to ensure deterministic execution, handling complex decision cycles and error recovery gracefully