Custom LLM Agent Development

LLM agent development services are professional engineering services that design, build, integrate and operate custom AI agents powered by large language models (LLMs) — GPT-4o, Claude 3.5 Sonnet, Llama 3.3, Gemini 2.0, Amazon Titan. The services span use-case scoping, LLM and framework selection (LangChain, LangGraph, AutoGen, CrewAI), agent architecture, function-call schema design, agentic RAG over your domain corpus, evaluation harnesses, guardrails, observability and integration into your CRM, ERP and back-office.

Three reasons: (1) Speed — DreamzTech ships production LLM agents in 4–14 weeks; in-house teams typically take 6–12 months and stall on prompt engineering and evals. (2) Multi-LLM expertise — we benchmark GPT-4o vs Claude 3.5 vs Llama 3.3 per task instead of locking you to one vendor. (3) Eval-driven engineering — we ship with eval harnesses, hallucination defense and observability from day one, not as afterthoughts.

A vanilla LLM API call sends a prompt, gets a response — no tool use, no memory, no actions. A RAG app retrieves documents from a vector store and adds them to the prompt — better grounding, still no actions. An LLM agent reasons, plans, calls tools, takes actions across systems, remembers across sessions, and reflects on tool outputs. LLM agent development services engineer the third category — the productionised loop, not just the prompt.

Every major foundation model: OpenAI (GPT-4o, GPT-5, o1, GPT-4 Turbo), Anthropic Claude (3.5 Sonnet, 3.5 Haiku, Claude 4), Meta Llama (3.1, 3.3 — 8B / 70B / 405B), Google Gemini (1.5 Pro, 2.0 Flash, 2.0 Pro), Amazon Titan, Mistral Large / Mistral Nemo / Codestral, Qwen 2.5, Cohere Command R+. We benchmark per use case based on accuracy, cost, latency and governance constraints and recommend the right model for each agent in your system.

LangChain for foundational toolkit (chains, retrievers, integrations); LangGraph for stateful multi-step agents with cycles; AutoGen for conversational multi-agent debate; CrewAI for role-based crews (researcher / writer / reviewer); LlamaIndex for agentic RAG; Semantic Kernel for .NET-native shops; and OpenAI Assistants API / AWS Bedrock Agents / Azure AI Agents for hyperscaler-native deployments. We mix and match per use case.

Five layers: (1) Constitutional AI guardrails reject ungrounded outputs at generation time. (2) Citation-grounded RAG forces answers from your vetted corpus with source attribution. (3) Function-call validation rejects malformed or out-of-schema tool invocations. (4) Confidence scoring routes low-confidence outputs to human review. (5) Continuous shadow-mode evaluation against ground-truth datasets catches drift. For high-stakes actions (financial, medical, legal binding), human-in-the-loop approval is mandatory.

Agentic RAG adds retrieval as a tool the LLM agent decides to use — the agent reasons whether and how to query the vector store, what to retrieve, and whether the retrieved context is sufficient before answering. This beats standard RAG (which always retrieves) for complex multi-hop questions, multi-document due diligence and chained reasoning. We engineer agentic RAG with hybrid search (BM25 + vector), Cohere / BGE re-ranking, source citation and confidence-based escalation.

Via tool servers exposing your CRM, ERP and back-office to the LLM. We engineer Model Context Protocol (MCP) servers — the emerging open standard — that connect Claude, GPT and Gemini agents to Salesforce, ServiceNow, SAP, Microsoft Dynamics 365, NetSuite, Workday and HubSpot. Agents authenticate via OAuth 2.0, respect record-level RBAC, log every action and support both human-in-the-loop and fully-autonomous execution.

A single-task tool-using LLM agent MVP starts at $25,000–$45,000 (LangChain, 2–3 tool integrations, 4–6 weeks). A production multi-LLM agent system runs $75,000–$200,000 (LangGraph or CrewAI, specialist agents, agentic RAG, observability, 5–10 integrations, 8–14 weeks). Enterprise LLM agent platforms with fine-tuning, multi-region deployment, FedRAMP / HIPAA controls and 24/7 SRE run $200,000–$400,000+.

A focused tool-using agent MVP (single workflow, 2–3 tool integrations) ships in 4–6 weeks. A multi-LLM agent system (3–5 specialist agents, 5–10 tool integrations, agentic RAG, observability) ships in 8–14 weeks. An enterprise platform with fine-tuning, compliance gates and 24/7 SRE — 14–22 weeks. All timelines include design, build, evals, integration, security review and production cutover with stage gates.

Both — and we help you choose. Prompt engineering covers 80% of enterprise use cases (cheaper, faster to iterate, no training required). Fine-tuning is the right call for: (1) consistent stylistic tone, (2) proprietary terminology, (3) latency-sensitive workloads where a smaller fine-tuned model beats a larger frontier model, (4) tasks where prompt engineering plateaus. We use supervised fine-tuning, LoRA, QLoRA and DPO on OpenAI, AWS Bedrock, Azure ML and self-hosted Llama / Mistral.

Every LLM agent ships with an eval harness: (1) ground-truth dataset of 50–500 labelled examples per agent task; (2) automated eval pipeline using LangSmith, Promptfoo, Braintrust or Ragas; (3) human-grading rubrics for subjective outputs; (4) faithfulness, relevance and groundedness metrics for RAG; (5) continuous shadow-mode testing in production that flags accuracy regressions. Evals run on every prompt change, model upgrade and weekly in production.

MCP is Anthropic’s open standard for connecting LLM agents to external tools, data sources and services — a “USB-C for AI tools.” It standardises how agents discover and call tools across providers (OpenAI, Anthropic, Google) without custom adapters per LLM. DreamzTech is an early MCP adopter — every LLM agent we engineer exposes its tools as MCP servers, so your agents are portable across foundation-model providers and your tools work across multiple agents.

Yes. We build voice LLM agents on OpenAI Realtime API, Anthropic Claude on Amazon Bedrock with voice gateways, and Azure AI Speech. Multimodal vision LLM agents on Claude 3.5 Sonnet, GPT-4o and Gemini 2.0 — agents that read photos, screenshots, PDFs and video frames. Common deployments: voice IVR replacement, vision-based claims processing, AR field-service copilots, multimodal customer support.

Compliance is engineered in, not bolted on. Infrastructure is SOC 2 Type II, ISO 27001 / 27018 attested with HIPAA BAAs across AWS, Azure, Google Cloud. Every LLM call is logged with immutable audit trails for SOX, HIPAA, GDPR and EU AI Act. PII / PHI is redacted before reaching public LLM endpoints. Private LLM deployment (Azure OpenAI, Bedrock zero-retention, self-hosted Llama / Mistral) is available for regulated finance, defense and healthcare. Every project ships with NIST AI RMF documentation.

We route per task based on accuracy, cost and latency. Claude 3.5 Sonnet for nuanced reasoning, contracts and long-context tasks. GPT-4o for code generation and function-calling DX. Llama 3.3 70B for cost-sensitive high-volume agents. Gemini 2.0 Flash for low-latency / high-context-window tasks. Amazon Titan for AWS-native deployments needing zero-retention. Our agent orchestration layer makes routing decisions automatically based on the task class, with eval-backed model A/B tests informing every routing rule.

Both. We engineer single LLM agents (one model, multiple tools — common for ticket deflection, lead qualification, contract Q&A) and multi-agent systems (multiple specialist LLM agents coordinating — researcher / writer / reviewer crews, planner-executor topologies, hierarchical supervisor-worker patterns). Multi-agent is the right call when no single prompt and tool-set can reliably handle the task — e.g., M&A due diligence across 50 documents needs a research-summarise-cross-check pipeline.

Four phases — the DreamzTech AGENT Framework: Assess & Govern (use-case discovery, NIST AI RMF scoping); Engineer (agent architecture, model + framework selection, tool inventory, function schemas, guardrails); Build, Fine-Tune & Evaluate (agent build on LangGraph / CrewAI / AutoGen, eval-driven prompt iteration, fine-tuning where it matters); Integrate, Operate & Tune (full agent-fronted application, observability, SRE runbook, SLA-backed support).

Yes — and that’s usually where the biggest ROI lives. We engineer LLM agent tool adapters for your existing REST, GraphQL, gRPC and SOAP APIs with OAuth / API-key authentication, retry / circuit-breaker patterns, structured output schemas and audit logging. For modern stacks we wrap your APIs as Model Context Protocol (MCP) servers — making them discoverable to any MCP-compatible LLM agent (Claude, GPT, Gemini) with a single adapter.

Five techniques: (1) intelligent model routing — cheap model for easy tasks, frontier model for hard tasks; (2) prompt caching (Anthropic prompt cache, AWS Bedrock caching) for repeated context; (3) response caching for deterministic queries; (4) fine-tuned smaller models replacing frontier-model prompts at 5–10× lower cost; (5) batched inference for high-volume offline workloads. Across deployments we typically deliver 40–70% LLM cost reduction without accuracy loss.

Managed LLM Agent Operations covers 24/7 production observability (LangSmith, Langfuse, Arize), prompt versioning and A/B testing, drift and hallucination monitoring, quarterly model upgrades (e.g., GPT-4o → GPT-5, Claude 3.5 → Claude 4), guardrail tuning, eval-set expansion, SLA-backed incident response and cost optimization. Three tiers — Bronze (business hours), Silver (extended), Gold (24/7 with named SRE).

Hyperscaler agent APIs (OpenAI Assistants, Bedrock Agents, Azure AI Agents, Google ADK) are good starting points for simple single-LLM agents — fast PoCs, low engineering overhead. Custom LLM agent development on LangGraph / CrewAI / AutoGen gives more control: multi-LLM routing across vendors, complex multi-agent topologies, custom guardrails, full observability and deeper CRM/ERP integration. We help you make the trade-off per use case — sometimes hyperscaler-native, sometimes custom.

Eight primary verticals — Healthcare (HIPAA-eligible prior-auth, clinical Q&A, FHIR copilots), BFSI (KYC/AML, AP automation, lending copilots), Legal (M&A due-diligence, clause extraction, CLM agents), Insurance (claims triage, fraud detection, underwriting), Retail (customer service, recommendation, inventory), Manufacturing (shop-floor copilots, supplier-doc QA), Public Sector (FedRAMP / GovCloud / IL5 agents) and HR/Talent (onboarding, employee self-service, recruiter copilots).

Book a free 30-minute LLM agent architect call. Bring your toughest LLM challenge — domain hallucinations, tool-call accuracy, latency, integration depth — and a senior architect will walk you through the recommended model + framework + RAG pattern, an eval benchmark on representative data, and a fixed-scope budget range. Then we send a written proposal within 1 business day with reference architecture, scope and engagement model. No sales pitch, no obligation.