AI Agent & LangGraph Engineer
Orchestrating reliable, autonomous multi-agent systems.
Production AI agents and multi-agent systems built with LangGraph: deterministic, self-correcting, observable pipelines, not fragile prompt scripts.
I build software and AI solutions, automate the manual work slowing teams down, and ship MVPs in weeks, not months.
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AI Agent & LangGraph Engineer — overview
Most "AI agents" are a single large prompt hoping for the best, and they fail unpredictably in production. Sharon Rosario builds agents the way reliable software is built: as explicit state machines with defined steps, verification, and self-correction — using LangGraph to make the control flow deterministic and debuggable.
The engineering challenge with agents is not getting a good answer once; it is getting a correct answer reliably, knowing when the agent is wrong, and recovering without human intervention. Sharon designs multi-agent pipelines — researcher, writer, verifier roles — with observability so failures are diagnosable rather than mysterious.
Agents that are actually production-ready
Explicit state and control flow with LangGraph instead of one monolithic prompt. Verification and self-correction loops so the system catches its own mistakes. Observability with tools like LangSmith so you can see exactly where a run went wrong. And guardrails so an autonomous system fails safely.
This turns "impressive demo" into "dependable feature" — the difference that decides whether agents can ship to customers.
Multi-agent orchestration
Sharon builds pipelines where specialized agents hand off state to each other — for example a Researcher-Writer-Verifier loop — with the coordination logic made explicit and testable. The LangGraph case study below walks through the full architecture and hardening patterns.
Related case studies
Orchestrating Multi-Agent Workflows with LangGraph
A deterministic, self-correcting Researcher-Writer-Verifier agent pipeline in LangGraph — with state management and LangSmith observability.
Read the case studyI Built a Claude Code Skill That Tells Me Not to Build Things
An open-source Claude Code skill with a validation gate that can return DON'T BUILD — four kill criteria, state on disk, and milestones sized to one conversation.
Read the case studyI Built a SynthID Watermark Detector That Cannot Detect Gemini
Reverse-engineering Google's SynthID-Text from the Nature paper, reaching ROC-AUC 1.0000 — then proving the feature cannot ship without the provider's key.
Read the case studySwitch Claude Code Accounts Without Logging Out, and Keep Every Conversation
Several Claude Code accounts on one machine, one command each, with every account sharing the same conversations, memory and settings.
Read the case studyAntigravity Account Switchers: Why Free Quotas Get Farmed, and What It Costs You
How Antigravity account switchers work, why free AI tiers get farmed, how Google enforces usage limits, and the security cost of storing every account's token.
Read the case studyFrequently asked questions
A single "God prompt" is non-deterministic and hard to debug. LangGraph models an agent as an explicit state machine with defined steps, verification, and self-correction, so the workflow is reliable, testable, and observable — production-ready rather than demo-only.
Specialized agents — for example researcher, writer, and verifier — pass state between defined steps with explicit coordination logic and self-correction loops, rather than one prompt trying to do everything at once.
Explicit state management, verification and self-correction, observability with tools like LangSmith, and guardrails so the system fails safely. The LangGraph case study documents these patterns in detail.
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