RAG retrieval quality plateau
Naive top-k retrieval plateaus quickly. We implement hybrid BM25 + embedding search, reranking with a cross-encoder, and HyDE for low-recall queries.
LangGraph RAG EU AI Act GDPR
LangChain Development and AI Agents for Production LLM Applications
LangChain and LangGraph are our tools of choice for production AI agents and RAG pipelines — not for proof-of-concepts. We instrument every chain with LangSmith, run RAGAS evals in CI, and design agents with explicit approval gates for actions that cannot be undone. Every engagement ships with EU AI Act risk classification on day one.
We deliver LangChain and LangGraph engineering for RAG pipelines over private corpora, multi-step AI agents with tool use, and LLM orchestration layers connecting OpenAI, Anthropic and self-hosted models. LangSmith observability is non-negotiable — every chain step is traced in production. For regulated industries, EU AI Act risk classification and GDPR data handling are part of the delivery, not a compliance afterthought.
Challenges
Industry challenges we solve
Agent hallucination on tool results
Agents fabricate plausible-sounding tool outputs when retrieval fails. We add schema validation on every tool response and implement explicit failure modes that surface to the agent.
Token budget overruns in long sessions
Uncapped conversation history blows token limits and costs. We implement tiered context management — summarisation, selective retrieval and per-turn budgets.
LangChain version fragmentation
LangChain's API surface changed significantly between 0.0.x and 0.3.x. We migrate incrementally, pin versions and track breaking changes before upgrading.
Agent loop runaway
Agents without explicit stop conditions loop indefinitely. We set max iterations, implement cycle detection in LangGraph and define explicit terminal states.
Tool permission surface
Agents with broad tool access create security risk. We assign minimal-privilege API keys per tool, require approval gates for irreversible actions and log every tool call for audit.
Solutions
Solutions we build
RAG over private corpora
Document ingestion, chunking, embedding and hybrid retrieval over internal knowledge bases — with source attribution and RAGAS-measured quality.
Multi-step AI agents
LangGraph agents that reason, call tools, branch on results and pause for human approval — for document processing, research and automation workflows.
LLM orchestration layers
Vendor-neutral routing across OpenAI, Anthropic and self-hosted models — with fallback, cost tracking and latency SLAs per model tier.
Conversational assistants
Chat interfaces over product documentation, internal knowledge and customer data — with conversation memory and source-cited answers.
Document extraction pipelines
Structured data extraction from PDFs, contracts and forms — schema-driven, with RAGAS faithfulness scoring and human review queues for low-confidence extractions.
Multi-agent systems
Supervisor-and-specialist architectures where a routing agent delegates to domain-specific sub-agents — for complex analytical and research tasks.
Stack
Technology stack
LangChain, LangGraph, LangSmith, LlamaIndex, OpenAI, Anthropic, pgvector, Qdrant, RAGAS, FastAPI, Python, Pydantic, Docker, Kubernetes.
Compliance
Compliance & regulations
GDPR-aligned · EU AI Act-aware · SOC 2-capable · HIPAA-capable · CCPA-acknowledged
EU
- EU AI Act — risk classification, technical file, transparency obligations.
- GDPR Art. 22 — automated decision-making, DPIA, human oversight.
- DSA — transparency for recommender and content-moderation systems.
- GDPR — data residency, ZDR endpoint configuration.
US
- NIST AI RMF — govern, map, measure, manage.
- CCPA/CPRA — automated decision opt-out.
- SR 11-7 — model risk management.
- HIPAA — minimum necessary, de-identification.
Shared: OWASP LLM Top 10, prompt-injection hardening, LangSmith tracing for audit.
Cases
Selected LangChain & AI Agents case studies
LegalTech · Mobile · CRM
Signatory Pro
Native iOS and Android e-signature clients with a Symfony + React CRM for a cross-border law firm — KYC onboarding and a defensible evidence trail for US & EU matters.
HealthTech · Endoscopy
ArgoView
Tablet-first endoscopy recording, patient records, and DICOM/HL7 export — built on Laravel + React with browser-tier WebRTC capture for US & EU clinics.
PropTech · Marketplace
ANT
Property marketplace web platform with listing CMS, search and B2B admin console for US and EU operators.
Why YuSMP
Why AI teams choose YuSMP
LangGraph in production
We have shipped LangGraph-orchestrated agents to production users — not just ran demos. State machines, interrupt gates and cycle detection built from real agent runaway incidents.
RAGAS eval from day one
Every RAG pipeline has a RAGAS eval harness before the first prompt goes live. Quality metrics gate every PR merge — no silent degradation.
EU AI Act-ready delivery
AI Act risk classification, technical file and DPIA preparation are part of our standard AI engagement — not extras billed separately.
FAQ
LangChain & AI Agents FAQ
LangChain or LlamaIndex — which framework do you use?
LangChain for agentic workflows with tool use, multi-step reasoning and complex chain composition. LlamaIndex for RAG-heavy workloads where the indexing pipeline, retrieval strategies and structured output extraction are the primary concern. We often use both in the same project — LlamaIndex for the retrieval layer, LangChain for agent orchestration above it.
LangGraph or standard LangChain chains?
LangGraph for anything requiring branching, looping, parallel tool execution or human-in-the-loop interrupts. Standard chains for linear prompt pipelines where the complexity overhead of a state machine is not justified.
How do you evaluate RAG quality?
RAGAS metrics: faithfulness (answer grounded in retrieved context), answer relevance, context precision and recall. We build the eval set from real user queries before writing the first prompt, run it in CI on every change, and alert when any metric drops below threshold.
How do you design AI agents safely?
Tool schema design is the first safety layer — we define what each tool can and cannot do, use minimal-privilege API keys, and require explicit approval for irreversible actions (send email, modify database, call external API with side effects). LangGraph's interrupt mechanism is our standard for human-in-the-loop gates.
How do you keep agent context within token limits?
Conversation summarisation with a dedicated compression LLM call, selective memory via a retrieval-augmented history store, and tiered context: always-on system context, recent messages, and retrieved relevant history. We profile token usage per agent step and set budgets per turn.
Can you build multi-agent systems?
Yes. We design multi-agent architectures where a supervisor routes tasks to specialist agents — a document agent, a calculation agent, a search agent — and aggregates results. LangGraph's subgraph feature handles agent-to-agent communication cleanly.
How do you debug LangChain applications in production?
LangSmith is our default observability layer — every LLM call, tool invocation and chain step is traced with latency, token count and inputs/outputs. We set up LangSmith from day one, not as a retrofit, and use it to catch regressions before they reach users.
Build production AI agents with LangChain engineers fluent in EU AI Act
Response within 1 business day. NDA on request.