AI Agent Development Services — Built to Run in Production.

We design, build, and deploy custom AI agents that act across your systems — safely, observably, and accountably.

What goes into building an AI agent.

Behind every production-ready AI agent sit four pieces — a model that reasons, tools it can call, guardrails it follows, and evals that prove it’s working.

Model

Model-agnostic LLMs — Claude, GPT, Gemini, or open-weight Llama / DeepSeek. Picked for the job, not the vendor — and pipelines designed so you can swap providers as the landscape evolves.

LLM providers supported

Tools

Typed integrations with your CRMs, ERPs, databases, and internal APIs — every action the agent can take is wired, validated, and tested before launch.

40+

Integrations shipped

Guardrails

Safety policies, PII redaction, and human-in-the-loop approval gates wired in from day one — enforced at the system level, not just in the prompt.

HITL

By default on every build

Evals

Accuracy scoring, drift detection, and live observability — continuous evidence the agent is still doing what it’s supposed to do in production.

100%

Evaluated before launch

AI agents built to take real work.

Customer Service AI Agents

Triage tickets, resolve repeat questions, escalate to humans only when it matters.

TriageSelf-serveSmart escalation

Lead Qualification AI Agents

Score inbound leads, route to the right rep, follow up automatically.

Lead scoringRoutingAuto follow-up

Claims & Document Agents

Read documents, extract structured data, flag exceptions for human review.

OCR + extractException flagsHITL review

Operations Copilots

Embedded in your CRM or ERP — automate the routine workflows your team does manually today.

In-CRMWorkflow autoApprovals

Multi-Agent Systems

A team of specialized agents that collaborate, hand off, and verify each other’s work.

OrchestrationHandoffsVerification

Voice Agents

24/7 voice-first agents that handle inbound calls, qualify leads, resolve support tickets.

Inbound calls24/7Multilingual

100%

Evaluated before launch — every agent ships with an eval harness.

Cloudmate standard

24/7

Live observability on every production agent.

Always-on

Model providers benchmarked per build.

Per engagement

40+

Tool integrations validated and tested.

Per agent

8 wk

Average time from kick-off to working production pilot.

Across engagements

30+

Production AI agents shipped across healthcare, retail, insurance and SaaS.

Cloudmate track record

How we build an AI agent, step by step.

Discovery

Scope the agent’s job, map the workflow, pick the right model.

Design

Solution architecture, tool schema, guardrail policy, eval plan.

Build

Wire tools, write prompts, design the planner, integrate with your stack.

Evaluate

Eval harness, accuracy scoring, HITL review, safety checks.

Ship

Production deployment, observability, daily drift monitoring.

The stack behind every AI agent we ship.

Models

Claude

GPT

Gemini

Llama

DeepSeek

Agent Frameworks

LangChain

LlamaIndex

DSPy

Semantic Kernel

Vector & Memory

Chroma

Qdrant

Weaviate

Redis

Cloud / Inference

AWS Bedrock

Cloudflare Workers AI

GCP Vertex AI

NVIDIA

Guardrails & Safety

Guardrails AI

NeMo Guardrails

Presidio

Lakera

Evals & Observability

Arize Phoenix

Langfuse

Weights & Biases

Ragas

The industries our AI agents work in.

Healthcare

HIPAA · HL7 · FHIR

Patient triage agents
Clinical documentation agents
Prior authorization agents

Retail & eCommerce

PCI-DSS · OMS · POS

Customer support agents
Order management agents
WhatsApp shopping agents

Insurance

IRDAI · SOC 2 · GDPR

Claims intake agents
Policy renewal agents
Fraud detection agents

Real Estate

CRM · MLS

Lead qualification agents
Mortgage screening agents
Tenant onboarding agents

The questions we get about AI agents.

How much does AI agent development cost?

Custom AI agent development typically ranges from $3,000 for a focused single-task agent to $20,000+ for a production-grade multi-agent system. The main cost drivers are tool integrations, eval depth, safety requirements, and the number of workflows the agent covers. We provide a fixed-scope quote after a two-week discovery.

How long does it take to build a production AI agent?

An 8-week average covers discovery, design, build, evaluation, and production deployment. A working prototype ships by week 3 — you see your agent acting on real data before major investment. Larger multi-agent systems run 8–15 weeks.

What is the difference between an AI agent and a chatbot or copilot?

A chatbot answers. A copilot suggests. An agent acts. AI agents plan multi-step tasks, call your tools (CRM, APIs, databases), verify their work, and escalate to a human only when needed — they don’t just generate text, they complete jobs end to end.

Can the agent work with our existing CRM, ERP, and internal APIs?

Yes. We build typed integrations to Salesforce, HubSpot, custom CRMs, SAP, NetSuite, WhatsApp Business API, and internal REST/GraphQL endpoints. Every action the agent can take is wired, validated, and observable — with role-based access and audit trails so your compliance posture stays intact.

Do you support Model Context Protocol (MCP)?

Yes — MCP-native by default. Model Context Protocol is the emerging open standard for connecting agents to tools, data sources, and external systems, and we ship MCP servers as the connector layer for every agent we build. That means your agent’s tool layer stays portable: swap the underlying model (Claude, GPT, Gemini, open-weights), change the agent framework, or extend with new tools later — the connectors don’t need to be rewritten. We also build custom MCP servers when you need agents to talk to internal systems that don’t already expose one.

How do you keep agents safe and prevent off-policy actions?

Three layers of defense: prompt-level safety policies and PII redaction; system-level guardrails using typed tools that only expose actions the agent is allowed to take; and human-in-the-loop approval gates on high-risk actions. We design HITL into every agent from day one — never bolted on after launch.

Which model should we use — Claude, GPT, Gemini, or open-weights?

Model choice depends on cost, latency, accuracy, and data-residency requirements. We build model-agnostic pipelines so you can switch providers as the landscape evolves. Most agents use Claude or GPT for reasoning, Gemini for long-context work, and open-weight Llama or DeepSeek for high-volume cost-sensitive workloads.

Can you build multi-agent systems?

Yes. We build orchestrated networks of specialized agents that hand off, collaborate, and verify each other’s work. Common patterns include a planner agent that decomposes the task, specialist agents that execute, and a verifier agent that gates the final output — all with full observability of the conversation between them.

Do you offer dedicated AI agent engineering teams?

Yes. Three engagement models: Dedicated agent squads (4–8 engineers, ongoing), Project-based delivery (fixed scope, fixed price), and Staff augmentation (senior agent engineers embedded in your team). Every squad includes an agent architect, backend engineer, evals specialist, and ML engineer.

Beyond AI Agent Development — services and solution.

Other services

GenAI DevelopmentCustom LLMs, RAG pipelines, copilots end to end.GenAI ConsultingStrategy, roadmapping, ROI modelling.Cloud MigrationAWS, Azure, GCP — zero-downtime migrations.

Solution · Built on Agents

AI Voice Agent

Not a service — a product. The voice-first AI agent that handles inbound calls, qualifies leads, and resolves support 24/7.

Explore the Solution

Have a workflow for an AI agent?

Tell us the task — we'll build an autonomous agent that runs it end to end.

Email Ushello@cloudmates.co Call or WhatsApp+91 96243 00546

AI Agent Development Services — Built to Run in Production.

We design, build, and deploy custom AI agents that act across your systems — safely, observably, and accountably.

Build Your AI Agent

What goes into building an AI agent.

Behind every production-ready AI agent sit four pieces — a model that reasons, tools it can call, guardrails it follows, and evals that prove it’s working.

Model

Model-agnostic LLMs — Claude, GPT, Gemini, or open-weight Llama / DeepSeek. Picked for the job, not the vendor — and pipelines designed so you can swap providers as the landscape evolves.

LLM providers supported

Tools

Typed integrations with your CRMs, ERPs, databases, and internal APIs — every action the agent can take is wired, validated, and tested before launch.

40+

Integrations shipped

Guardrails

Safety policies, PII redaction, and human-in-the-loop approval gates wired in from day one — enforced at the system level, not just in the prompt.

HITL

By default on every build

Evals

Accuracy scoring, drift detection, and live observability — continuous evidence the agent is still doing what it’s supposed to do in production.

100%

Evaluated before launch