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I will architect compliant autonomous multi agent systems using langgraph and crewai
Abdullah Khan
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About this gig
I architect deterministic Multi-Agent Systems that transition POC into robust, production-grade ecosystems, reducing operational overhead by 40%.
My engineering approach focuses on building autonomous cognitive architectures using
LangGraph and CrewAI that execute complex business logic with absolute reliability. I engineer collaborative agent swarms capable of planning, reasoning, and tool execution.
I possess deep domain expertise in highly regulated sectors, designing "Human-in-the-Loop" governance protocols to ensure high-uncertainty decisions are routed for verification.
My background involves engineering persistent state management and semantic memory layers, enabling agents to maintain context across long-running asynchronous workflows.
I architect secure, self-healing systems tailored to your specific operational bottlenecks.
If you require a scalable, secure, and high-performance AI architecture, I invite you to contact me to discuss your specific requirements.
Get to know Abdullah Khan
Abdullah Khan
AI Architect: 5 years, Enterprise RAG Systems, Agents and AWS MLOps
- FromPakistan
- Member sinceJul 2024
- Avg. response time2 hours
- Last delivery1 week
Languages
Urdu, English
I architect Enterprise RAG & Agent systems that cut operational overhead by 40%. From Proof-of-Concept to Production, I build scalable compliant infrastructure.
My focus is on eliminating enterprise bottlenecks: high hallucination rates, latency, and spiralling costs.
I possess deep domain expertise in highly regulated sectors. I design "Human-in-the-Loop" architectures that ensure high-uncertainty decisions for review, ensuring safety and compliance.
If you require a scalable, secure, and high-performance AI architecture, I invite you to contact me to discuss your requirements.
My Portfolio
FAQ
How do you prevent agents from entering infinite loops or hallucinating?
I engineer deterministic state machines using LangGraph, enforcing strict transition logic and "Human-in-the-Loop" checkpoints. This ensures agents execute within defined boundaries, preventing infinite loops and unauthorized actions common in basic autonomous business logics.
What is the strategic advantage of a Multi-Agent System over a single LLM?
A single LLM hallucinates when overloaded with context. Multi-Agent Systems assign specific roles to distinct agents. This "separation of concerns" drastically improves accuracy, reduces latency, and allows for parallel business tasks automation.
Do your agents possess long-term memory and context retention?
Yes. I engineer persistent state management using Redis (Short-term) and Vector Databases (Long-term/RAG). This allows agents to recall past interactions, user preferences, and institutional knowledge, enabling complex, multi-session workflows.
How do you manage token consumption and operational costs?
I implement cost-aware routing architecture. Simple tasks are routed to efficient models (Llama 3/GPT-4o-mini), while complex reasoning uses GPT-4o. I also deploy semantic caching to prevent redundant API calls, reducing operational overhead by up to 40%.
Can these agents perform actions on my internal software?
Absolutely. I build Custom Toolkits (APIs) that allow agents to securely interact with your internal CRM, ERP, or Database. I implement strict OAuth2 authentication and permission layers so agents can only perform authorized actions (e.g., Read-Only vs. Write).
