Custom AI Agent Development
We build autonomous and semi-autonomous AI agents engineered for specific, high-value tasks — research, code inspection, lead qualification, and data synthesis. We equip agents with stateful memory, retrieval, and strict guardrails to maintain reliability under real-world production conditions.
Best for: Organizations that have outgrown generic chat interfaces and need specialized AI agents integrated directly into their business software stack.
Purpose-Built Autonomous Agents That Survive Production
Generic off-the-shelf AI models fail when subjected to proprietary domain logic, strict data privacy constraints, and complex multi-step execution paths. Production-grade autonomous agents require structured tool execution, stateful memory management, and sandboxed execution environments. We develop custom AI agents using state-of-the-art LLMs, vector database retrieval (RAG), and rigorous evaluation benchmark harnesses—enabling intelligent agents to interact safely with your internal APIs, databases, and business logic.
What's included
Agent architecture & memory design
Designing stateful agents with short-term context windows and long-term vector/relational memory for persistent goal execution.
Tool integration & API execution
Granting agents safe, sandboxed tools to execute database queries, search documentation, generate reports, or call external APIs.
Model selection & multi-LLM routing
Selecting and routing prompts across Claude, OpenAI, or open-weights models (Llama 3, Mistral) based on task complexity, speed, and cost.
Rigorous evaluation benchmark suites
Automated test harness datasets that evaluate agent accuracy, reasoning steps, tool choice, and response consistency before every deploy.
Guardrail design & safety boundaries
Strict input/output sanitization, system prompt hardening, rate limits, and fallback routines to prevent agent divergence or hallucination.
How we deliver
A phase-gated engineering process designed for transparency, zero compliance surprises, and rapid velocity.
Task Scoping & Data Audit
We isolate the specific operational task to automate, audit source data quality, and define strict evaluation benchmarks for accuracy and speed.
Real-Data RAG Prototyping
We build a rapid working prototype against your actual data, testing vector retrieval, embeddings, and prompt strategies before full build.
Guardrails & Human-in-the-Loop
We install input/output firewall proxies, hallucination controls, automated evaluation benchmark suites, and fallback human approval gates.
Production Deploy & Monitoring
Deployment to production with continuous token expenditure tracking, latency monitoring, and automated knowledge base update webhooks.
Questions people ask
Which AI providers do you build with?
We are vendor-neutral. Depending on your privacy requirements and budget, we build with Anthropic Claude, OpenAI, or open-weight models (Llama 3, Mistral) hosted on your cloud.
How do you keep an agent reliable and prevent hallucinations?
Narrow scope, structured JSON outputs, evaluation benchmark suites against real examples, guardrails on inputs and outputs, and tool verification steps.
How does agent stateful memory work across long multi-step workflows?
We engineer hybrid memory architectures using Redis and vector stores so agents maintain session context and retrieve factual history across multi-turn tasks.
Can agents interact with our internal SQL/NoSQL databases securely?
Yes. Agents operate through sandboxed tool functions with read-only scopes or strict permission gates rather than raw database execution.
Can we host open-weight AI models on our own cloud infrastructure?
Yes. We deploy and optimize open-weight models like Llama 3 or DeepSeek on your AWS/GCP GPU instances (vLLM / Ollama) for complete data sovereignty.
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A new era of software risk. Ship past it with Lumyte.
Tell us what you're building or what's breaking. We'll reply with next steps, not a sales deck.
- hello@lumyte.com
- Phone
- +91 72330 30040
- Studio
- Patel Nagar, NeelmathaLucknow, Uttar Pradesh 226002