LLM & AI Agent Development
Custom LLM integrations and multi-step agent systems built for production, not demos. From OpenAI and Anthropic API integrations to LangGraph pipelines with full evaluation coverage.
Most LLM integrations fail between the demo and the first production incident.
The prompt works in the playground. It breaks at scale because edge cases weren't in the test set, retrieval degrades on real documents, and confidence thresholds were never defined. Most teams fix this reactively: a production incident, a round of prompt patching, a rollback. We build the evaluation harness, retrieval design, fallback paths, and confidence calibration before the first production deploy, so the system handles the real world on day one.
LLM and agent development is where most teams underestimate the gap between a working prototype and a reliable production system. We build that layer: prompt architectures that hold under adversarial input, retrieval pipelines tuned on held-out eval sets, multi-step agents with state persistence and human-in-the-loop checkpoints, and the observability to catch model drift before users file tickets. We've shipped LLM-first products across news, healthcare, legal, and enterprise SaaS — and we know the difference between a demo that impresses a boardroom and a system that earns trust at scale.
Capabilities
LLM API integrations
Production-ready integrations with Anthropic (Claude), OpenAI (GPT), Mistral, and open-weight models via Ollama or vLLM. Streaming, function calling, structured outputs, and the retry and rate-limit handling that keeps your system up under load.
Multi-step agent pipelines
Agentic systems that plan, call tools, read results, and decide next steps — built on LangGraph or first-party SDKs with full state persistence, memory management, and human-in-the-loop escalation at configurable confidence thresholds.
RAG system design and build
Retrieval-augmented generation tuned for your corpus: chunking strategy, embedding model selection, hybrid retrieval (semantic + keyword), reranking, and the held-out eval set that proves it. Not a template — designed to your documents and query patterns.
Prompt engineering and management
Structured prompt libraries with versioning, A/B testing, and regression tracking. We treat prompts as first-class code artefacts: reviewed, tested, and deployed through CI with eval gates, not edited live in production.
LLM evaluation harnesses
Custom eval frameworks (RAGAS, Braintrust, or bespoke) that score every release on your domain-specific prompt set. Evaluation is a gate, not a retrospective: a failing score blocks the deploy.
Streaming and real-time LLM UX
Client-side streaming implementations (SSE, WebSockets) with skeleton states, confidence indicators, citation displays, and graceful fallback paths — the UX layer that turns a model response into a trustworthy user experience.
How we build it
Tools and technologies we use in this practice, chosen for fit, not familiarity.
Our process
Consistent across every engagement, adapted to your constraints, not the other way around.
Capability scoping and eval design
Before writing any code, we define what 'good' looks like: the user jobs the LLM owns, the failure modes it must not produce, and a held-out evaluation dataset built from your real queries and documents. This becomes the specification every sprint is scored against.
Iterative build with eval gates
Two-week cycles: build a narrow capability, run it against the eval set, measure quality on held-out examples (not cherry-picked demos), and iterate. You see the score delta at every review, and the specific failure cases driving the next sprint's focus.
Production hardening and handoff
Confidence thresholds, fallback paths, prompt versioning, evaluation CI, latency and cost dashboards, and drift monitoring. We do not consider an LLM system shipped until it has observability that catches degradation before users notice it.
Questions teams ask before they start
What are LLM agents?
LLM agents are AI systems that use large language models as a reasoning engine to plan, make decisions, and take actions — calling tools, querying databases, browsing the web, or triggering other systems — in pursuit of a defined goal. Unlike a simple chatbot, an agent can decompose complex tasks, retry on failure, and operate autonomously over multiple steps.
What are common use cases for LLM agents in business?
Common enterprise use cases include: AI research agents that gather and synthesise information; customer support agents that resolve queries end-to-end; sales agents that enrich CRM records and draft outreach; compliance agents that monitor documents for regulatory changes; and operations agents that orchestrate multi-system workflows without human handoffs. 7code builds purpose-built agents for each context.
How much does it cost to build an LLM agent?
Costs depend on scope and complexity. 7code provides fixed-price Discovery Sprints so clients understand architecture and cost before committing to the full build. Contact office@7code.ro for a scoped estimate.
How long does it take to build an LLM agent?
A production-ready single-purpose LLM agent typically takes four to eight weeks to build, including discovery, architecture, development, evaluation, and deployment. Multi-agent systems or agents requiring deep integration with enterprise systems may take twelve to twenty weeks. 7code uses two-week sprint cycles with working demos at the end of each sprint.
What is the difference between prompt engineering and fine-tuning for LLM agents?
Prompt engineering shapes model behaviour through careful instruction design — it is fast, cheap, and reversible. Fine-tuning retrains a model on domain-specific data to bake in specialised knowledge or style — it is slower, more expensive, and harder to iterate. 7code defaults to prompt engineering and RAG for most business cases, reserving fine-tuning for when retrieval cannot meet accuracy requirements.
How does 7code handle hallucination and safety in LLM agent systems?
7code implements guardrails at multiple levels: retrieval-augmented generation (RAG) to ground answers in verified data, output validation layers that check agent responses before acting, confidence thresholds that trigger human escalation, and audit logging of all agent decisions. For high-stakes domains — healthcare, finance, legal — additional human-in-the-loop checkpoints are mandatory design requirements.
What are multi-agent systems and when does 7code recommend them?
Multi-agent systems are architectures where multiple specialised AI agents collaborate — one researches, one writes, one validates — coordinated by an orchestrator. 7code recommends multi-agent designs when tasks are too complex for a single agent, when parallel processing is needed for speed, or when separation of concerns reduces hallucination risk in high-stakes workflows.
Which LLM does 7code recommend — OpenAI, Anthropic, or open-source?
Model selection depends on the use case. OpenAI GPT-4o suits broad reasoning and tool use. Anthropic Claude excels at long-context tasks and safety-critical applications. Open-source models (Llama, Mistral) are preferred when data must remain on-premises or costs at scale demand it. 7code is model-agnostic and selects based on accuracy, cost, latency, and compliance requirements.
Projects using this service
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