I will build a langgraph ai agent with real state, retries and error handling

G
gaurav_sinha_31
G
gaurav_sinha_31
Gaurav S

About this gig

Most "AI agents" are a prompt in a while-loop. They work in the demo, then hit a real user and hang, loop forever, or silently return nothing - because there is no state, no retry path, and no defined behaviour for failure.


I build agents as actual state machines. Every step is a node, every failure has an edge, and the graph can loop deliberately instead of by accident.


I built a corrective-RAG copilot this way: an LLM grades each retrieved chunk, and if grading kills the retrieval the graph loops back with a rewritten query rather than answering from what survived. A chain cannot express that cycle. That is the entire argument for building it as a graph.


Backend engineer by day - Spring Boot, Go and Kafka in production at a US investment bank and a US insurer - so what I hand you is a service with error handling, not a notebook.


WHAT I NEED FROM YOU

What the agent must do, what tools or APIs it calls, and what it should do when a step fails. If you do not know the last one yet, that is fine - deciding it is part of the work.

Get to know Gaurav S

Gaurav S

Backend AI Systems Engineer

5.0(1)
  • FromIndia
  • Member sinceFeb 2024
  • Avg. response time2 hours
  • Last delivery2 years
  • Languages

    English, Hindi
Backend engineer with nearly four years of production experience building event-driven systems and shipping LLM-backed services into live products at a US investment bank and a US insurance provider. I specialize in what most AI work skips: putting a model inside a service that already has traffic, tests and a release process it cannot break. Retrieval quality, evaluation harnesses, defined failure behavior. Spring Boot, Go, Python, Kafka, Kubernetes, AWS, LangChain, LangGraph, RAG, vector databases, QLoRA. Available full time.

My Portfolio