LangGraph agent orchestration
Multi-step agents that read context, take the next step, and escalate the calls only people should make.
Soba Labs is a team of official LangChain Ambassadors and Experts. Custom agentic systems on the LangChain stack. Production-ready AI, not prototypes.
Multi-step agents that read context, take the next step, and escalate the calls only people should make.
Hybrid RAG and durable memory over your docs, calls, and tickets, grounded in real sources.
LangSmith evals, tracing, and monitoring so the system stays accurate as models and data change.
Confidence gates, review points, and audit trails for agents that touch real operations.
We also train teams with practical AI workshops built around the tools they already use.
Soba Labs is an AI-native software development company and a team of official LangChain Ambassadors and Experts. We build custom agentic systems and workflows on the LangChain stack that solve real business problems for mid-sized and growing companies, tailored to each client and delivered fast at a fixed price.
Yes. Soba Labs are official LangChain Ambassadors and Experts, led by founder Simon Budziak, a LangChain Ambassador for Poland and a former CTO. We work across the LangChain stack: LangGraph for agent orchestration, retrieval and RAG, and LangSmith for evals and observability.
A production agentic system is software, built with AI agents, that runs real work in production rather than a prototype. It reads context, takes the next step, and escalates the exceptions to a person, with confidence gates, evals, and monitoring that keep it reliable as models change.
We sell outcomes, not hours: a fixed price on a defined outcome, agreed before we start and delivered in days. Every system is custom to your process and your data, and you own it and its source, with no lock-in.
We model the agent as an explicit LangGraph state machine, trace every run in LangSmith, and gate each prompt, model or retrieval change on an evaluation set built from real conversations. Side effects such as CRM writes get idempotency keys, and uncertain cases route to a person instead of a guess. The Open Loyalty case study shows the whole loop.
Yes. We set up LangSmith tracing, evaluation datasets, calibrated LLM-as-judge evaluators and production feedback loops for LangChain and LangGraph agents, either inside a system we build or on an agent your team already runs. See LangSmith consulting.
The whole stack: LangChain for fast agent starts, LangGraph when the workflow needs explicit state and control, Deep Agents for long-running tasks that plan and delegate, and LangSmith for tracing and evals, plus production deployment. We pick the lightest one that does the job.
The reasoning core of a multi-agent localisation pipeline: a structured client rule base, the agents that reason over it, and producer approval at key decision points.
A production sales agent that qualifies inbound leads, answers product questions from a live knowledge base, and writes structured outcomes back to the CRM.
A patient-facing assistant that explains health scan results from an approved medical knowledge base. Low-confidence and clinical questions route to a clinician.
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We sell a fixed price on a defined outcome, delivered in days. Every system is custom to your process and your data, and you own it and its source. No product to license, no platform to rent, no lock-in.
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