Available Now · 90+ Readymade Solutions

AI Agent Development Company

Autonomous · Multi-Agent · Reliable

Miracuves is an enterprise AI agent development company. We deploy high-performance cross-platform applications in 3–9 days using our curated base of 90+ white-label clone solutions — delivering 100% source code ownership with absolute IP safety on day one.

9,000+ Delivered 3,900+ Store Published 100% Source Ownership NDA Day One
WhatsApp — Talk to Our Team Explore Clone Solutions
Clutch Reviewed 4.9★ · Starting from $3,699 · View live deployments
Miracuves Delivery RecordAI Agent Team
3–9d
Delivery timeline
$3,699
Starting price
90+
Clone solutions
100%
IP assignment
AI agent engineers active right now

Customer Support

Support Agent System

Sales Automation

Outbound Agent Workflow
AI Agent Control Plane ACTIVE (V1.0)
FRAMEWORK LangGraph + AutoGen
ORCHESTRATION Supervisor / Team
TOOL INTEGRATION 20+ Pre-built Tools
MEMORY RAG + Vector Store
20+ AI Agent EngineersAvailable for your project
30+ Agent SystemsDeployed by Miracuves
6-12 Week DeliveryBrief to production agent
LangGraph+AutoGen CertifiedOfficial partner stack
95% Task CompletionAverage agent accuracy rate

White-Label Ready

Fully rebrandable on delivery

NDA Day One

IP protected first call

Full Source Code

Delivered at handoff

60-Day Support

Post-launch included

100% IP Ownership

Yours — always

Clutch Reviewed 4.9★

Third-party verified
More than 3900+ Companies Trust us Worldwide
Our AI Agent Approach

How Miracuves delivers AI agent systems — from 30+ deployments of real experience

After deploying 30+ production agent systems and processing millions of automated tasks, Miracuves has a specific way of building AI agents. We start from battle-tested agent architectures — already integrated with memory systems, tool APIs, guardrails, and monitoring — not from a blank notebook.

Our agent stack combines LangGraph for orchestration, AutoGen for multi-agent collaboration, and CrewAI for role-based task delegation. Every agent is built with ReAct reasoning, persistent memory (RAG + vector store), and built-in error recovery. One architecture, multiple deployment targets, full source code yours on handoff.

Who this service is built for: Engineering leaders and product teams automating customer support, data analysis, code review, sales outreach, research, or business workflows. Miracuves AI agent development fits when you want intelligent automation with published pricing, full IP ownership, and a company accountable for reliability — not individual contractors. If your task is purely deterministic (same inputs produce the same outputs every time), a simpler rule-based system may be more cost-effective. We will tell you before any commitment.

Multi-agent supervisor architecture on every project — reliable orchestration, easy scaling
ReAct reasoning with self-reflection enforced — agents validate before acting, recover from errors
Three-tier memory (RAG + session + entity) when context persistence is required across conversations
CI/CD pipeline via GitHub Actions configured on every project — automated testing from first commit
Grafana + Prometheus monitoring deployed on every agent — accuracy, latency, error rate dashboards

From our AI agent team — US eCommerce support system, 8 weeks

"Multi-agent support system processing 15,000+ monthly tickets across Zendesk, Slack, email, and live chat. We built three specialized agents — triage, search, escalation — orchestrated by a LangGraph supervisor. RAG knowledge base indexed 400+ product documents in Pinecone. Human-in-the-loop for refunds and cancellations. 85% auto-resolution from day one. Delivered week 8."

Written by the Miracuves AI Agent Team · May 2026 · View Deployed Portfolio →
2M+
Monthly automated tasks processed by Miracuves agents
85%
Average auto-resolution rate across all deployed agents
60%
Lower operational cost vs manual workflows
30+
Production agent systems deployed to date
6-12w
Miracuves delivery timeline for scoped agent projects
4.9★
Clutch average rating across all agent projects
LangGraph
Graph orchestration
AutoGen
Multi-agent chat
CrewAI
Task delegation

Why AI Agents at Miracuves

Time to first deployment6-12 weeks
Frameworks supportedLangGraph · AutoGen · CrewAI
Cost saving vs manual workflowsUp to 60%
Agent architectures ready30+ patterns
Task completion accuracy95%+
Source code ownership100% yours
30+ Agent Systems Deployed

What Miracuves has built — what you can launch today

View All Agent Solutions →
6 Days 01

Generative AI

ChatGPT Clone

Fully functional AI assistant with deep chat context, prompt templates, API hooks.

From $3,299API IntegratedAI Chatbot
6 Days 02

Short Video

TikTok Clone

Scrollable short-video feeds, creator monetization, viral challenges, AI recommendations.

From $4,899iOS + AndroidCreator Dashboard
6 Days 03

Ride-Hailing

Uber Clone App

Live GPS dispatch, real-time driver bidding, and dual passenger/driver interfaces.

From $2,8993 AppsiOS + Android
6 Days 04

Streaming

Netflix Clone

Video streaming platform with tiered subscriptions, user profiles, content library.

From $3,699iOS + Android + WebMulti-Profile
6 Days 05

E-commerce

Amazon Clone

Multi-vendor marketplace with product catalog, cart, secure payments, reviews.

From $2,899iOS + AndroidMulti-Vendor
6 Days 06

Super App

Gojek Clone

Modular architecture handles 20+ services in one app — rides, delivery, payments.

From $3,69920+ ServicesMulti-City
Honest note: AI agents excel at structured, well-defined tasks. For creative brainstorming or tasks requiring deep subjective judgment, human-in-the-loop design is essential. Miracuves always recommends the right automation level — we tell you when a rule-based system or human-only approach fits better.
Technology Comparison

AI Agent vs Rule-Based Automation vs Generic LLM API — which is right?

Most AI companies pitch agents as the answer to everything. Miracuves compares honestly — your architecture choice determines reliability, cost, and maintenance.

Metric AI Agent · LangGraph
← MIRACUVES DEFAULT		 Rule-Based Automation Generic LLM API
Autonomy Level Full — tool use, planning, memory, self-correction None — rigid if/then flows only Architectureless — single-turn completions
Multi-Step Reasoning Native — ReAct, reflection, sub-agent delegation Linear — no branching intelligence Manual — prompt engineering only
Tool Integration 20+ pre-built tools, custom tool SDK Pre-configured connectors only None — raw API calls
Memory & Context Persistent — RAG, vector store, session memory No adaptive memory Limited — context window only
Best For Complex workflows · dynamic tasks · scale Simple · predictable · high-volume Single-turn · prototype · low cost

Choose AI Agent if…

Your task requires multi-step reasoning, dynamic decision-making, or integration with multiple tools/APIs. You need memory, planning, and the ability to recover from errors autonomously.

Consider an alternative if…

Your workflow is entirely deterministic (same inputs = same outputs every time) or your budget cannot support LLM inference costs. See Python Automation →

Technical Architecture

How Miracuves engineers structure AI agent systems for production

These are the specific decisions our engineering team makes on every AI agent project — choices that determine whether a system scales, stays reliable, and maintains accuracy.

Architecture — Multi-Agent Supervisor Pattern

A supervisor agent orchestrates specialized worker agents via structured handoffs. Each worker owns a domain (search, code, data, email) with its own tools and memory. This is how Miracuves deploys a new agent workflow in 4 weeks without breaking existing operations.

Reasoning — ReAct with Reflection

Every agent uses the ReAct (Reasoning + Acting) loop with self-reflection. The agent thinks, acts, observes the result, and adjusts. The most common problem inherited from other builds: agents that hallucinate because they never validate tool outputs. We eliminate this with structured verification on every step.

Memory — RAG + Session + Entity Stores

Three-tier memory system: vector store (Pinecone/ChromaDB) for domain knowledge, session memory for conversation context, and entity store for persistent user/customer data. Every query is enriched before reaching the LLM.

What most AI agent builders get wrong

No error recovery in agent loops. No human-in-the-loop for critical decisions. Flat tool lists without prioritization. Single-agent for everything. Miracuves has inherited every one of these — correct architecture from day one is always faster than retrofitting reliability.

customer_support_graph.py — LangGraph
# Multi-agent supervisor pattern # Used in Customer Support Agent + all agent systems from langgraph.graph import StateGraph, END from typing import TypedDict, Literal class AgentState(TypedDict): messages: list next_agent: str tools_output: dict def supervisor(state: AgentState): """Route to the right worker agent.""" return {"next_agent": "router"} def search_agent(state: AgentState): """Knowledge base search worker.""" query = state["messages"][-1] results = vector_store.similarity_search(query) return {"tools_output": {"search": results}} def triage_agent(state: AgentState): """Classify and route customer intent.""" intent = llm.classify(state["messages"][-1]) return {"next_agent": intent} # Build the graph builder = ArchitectureGraph(AgentState) builder.add_node("supervisor", supervisor) builder.add_node("triage", triage_agent) builder.add_node("search", search_agent) builder.set_entry_point("supervisor") # Conditional routing with human-in-the-loop builder.add_conditional_edges( "supervisor", lambda s: s["next_agent"], {"router": "triage", "END": END} )
Multi-agent supervisor graph using LangGraph. Each worker agent has isolated tools and memory. Human-in-the-loop intercepts before irreversible actions. Used in every agent system Miracuves ships.
Our Service Models

Three ways Miracuves delivers your AI agent project

Every engagement is with Miracuves as a company — a complete team, a defined process, and full delivery accountability. Choose the model that matches your project stage.

Most Popular
Customer
Driver App
Admin

Readymade Clone · Fixed Price

White-Label Clone Delivery

Miracuves deploys a production-grade clone under your brand — iOS + Android + Admin Panel — in 3–9 days. Source code fully yours.

Starting from $2,499 — fixed price, no surprises
90+ solutions matched to your vertical
Branding, configuration, white-labelling applied
Admin panel included in every delivery
Full source code · NDA · 60-day support
MaterialApp Agent Tools LLM Memory Tools API Guardrails

Custom Development · Scoped

Custom Agent Build

Miracuves builds from your specification — custom architecture, custom flows, unique features. Full team: engineer, backend, QA, PM.

Scoped and priced before development begins
Clean architecture designed specifically for your product
Weekly sprint demos — working software every sprint
App Store and Play Store submission managed
Full source code · IP 100% yours
Wk 1
Wk 2
Wk 3
Wk 4

Ongoing Retainer · Monthly

Ongoing Agent Development

Miracuves works as your ongoing development partner — new features, releases, maintenance on a monthly retainer with weekly sprint demos.

From $2,299/month — cancel with 2 weeks notice
Dedicated Miracuves team assigned to your product
Direct communication — no account manager relay
Weekly sprint demos — deliverables every cycle
Scales up or down as your product evolves
Quality Standards

How Miracuves ensures every AI agent delivery meets production standard

Every project passes through Miracuves' quality gates before handoff — not as a checklist, as a non-negotiable delivery standard applied to every codebase we ship.

Clean architecture — Presentation / Domain / Data separatedArchitecture
Agent architecture — supervisor pattern with tool isolationArchitecture
ReAct reasoning — structured verification on every stepReasoning
Human-in-the-loop — critical decisions require approvalQA
CI/CD pipeline — automated builds and tests from day oneDevOps
No secrets in source — API keys in environment config onlySecurity
App Store-ready — provisioning, compliance, review passedDelivery
Enforced QA Gates

Our 6 Continuous Delivery Gateways

Every line of code, asset asset, and build profile must successfully clear all six quality control gates before repository handoff.

01

Code Review on Every Pull Request

Every line merged into your main branch is reviewed by a senior Miracuves engineer. No untested code reaches your production environment under any circumstances.

02

Automated Test Coverage Required

Unit tests for business logic, widget tests for UI components, and integration tests for critical user flows. Minimum coverage enforced before any release build is created.

03

Release Builds Profiled — Not Debug Builds

Miracuves profiles performance using agent profiling tools on every release build. Debug build performance is not representative of what users experience and is never accepted as sufficient.

04

Handoff Package — Not Just a Repository

Source code, documentation, environment setup guide, API documentation, deployment credentials, store credentials, and post-launch runbook — all included in every project handoff.

05

App Store Submission — Full Compliance Managed

Miracuves handles provisioning profiles, signing certificates, store listing creation, screenshot assets, compliance checks, and App Store review coordination for both iOS and Android.

06

Post-Launch Monitoring — 60-Day Active Support

Crashlytics and Firebase Analytics configured pre-launch. Miracuves monitors crash rates and performance metrics during the 60-day post-launch support window — proactive, not reactive.

Technology Stack

The AI agent stack Miracuves ships with

Matched to your agent architecture and deployment requirements — not a one-size-fits-all default.

LangGraph
Agent orchestration · graph-based workflows
AutoGen
Multi-agent conversations · code agents
CrewAI
Role-based agent teams · task delegation
OpenAI Agents SDK
Agent lifecycle · tool execution · guardrails
LangChain
LLM abstraction · chains · prompt management
Python 3.12+
Core language · async · type-safe
FastAPI
Agent API layer · async endpoints
Docker
Containerization · isolated agent runtimes
Kubernetes
Orchestration · auto-scaling agent pods
Redis
Session memory · caching · pub/sub
Pinecone
Vector store · semantic search · RAG
ChromaDB
Lightweight vector DB · local embedding
PostgreSQL
Relational data · agent state · audit logs
Grafana
Agent metrics · dashboards · monitoring
Prometheus
Metrics collection · alerting · tracing
AWS
Cloud infrastructure · Bedrock · EKS
Our Process

From brief to deployed AI agent — what happens and when

Every AI agent engagement follows the same delivery spine — whether you start from a readymade clone or a custom spec. You always know what Miracuves is doing, what you need to provide, and what gets delivered at each step. Timelines below reflect our standard clone sprint; custom builds run milestone-based with the same checkpoints.

Brief & NDA

Share your concept via WhatsApp. NDA signed same day. We ask 6 specific questions.

Step 01

Scope & Plan

Right solution base, stack, and model confirmed. No payment before scope is agreed.

Step 02

Build & Demo

Repo created, architecture set. First commit in 24h. Weekly working demo runs.

Step 03

QA & Polish

Tested on real iOS/Android devices. Profiles optimized for Store guidelines.

Step 04

Launch & Handoff

Full code and docs delivered. Store submissions handled. 60 days active support.

Step 05
Same DayNDA turnaround
3–9 DaysMVP Sprint delivery
24 HoursFirst commit after scope
60 DaysPost-launch support
Transparent Pricing

What AI agent development costs at Miracuves

We publish prices because we are confident in what we deliver. No "contact us for pricing" pages. No hidden fees after scope is agreed.

Readymade Clone

$2,499 from

Fixed price · 3–9 day delivery · scoped

  • AI agent system
  • Admin panel included as standard
  • Branding and white-label applied
  • Full source code on handoff
  • 60-day post-launch support
  • NDA protected from day one
Start a Clone Project
Most Requested

Custom Agent Build

Custom Quote

Scoped before build · milestone billing

  • Full AI agent team — engineer + backend + QA
  • Custom architecture for your spec
  • Weekly sprint demos — working software
  • App Store and Play Store submission
  • Full source code · complete IP transfer
  • Milestone billing — no pay before delivery
Get a Scope & Quote

Ongoing Development

$2,299/mo

Monthly retainer · cancel with 2 weeks notice

  • Miracuves team assigned to your product
  • New features, releases, and maintenance
  • Weekly demos and sprint planning
  • Direct communication — no relay
  • Scales up or down as needed
  • All code remains 100% yours
Discuss Ongoing Work
Why Miracuves publishes prices: Clients who understand cost upfront make better product decisions. If your project requires a larger budget, Miracuves will explain exactly why — not simply charge more.

What affects AI agent project cost at Miracuves

Readymade clone pricing stays fixed when scope matches the base product. Custom Agent builds scale with: number of user roles (customer, driver, admin, vendor), real-time features (live GPS, chat, video), payment and compliance integrations (BaaS, KYC, multi-currency), multi-city or multi-language rollout, and third-party SDKs beyond the standard stack.

Typical AI agent budget ranges

Readymade clone: from $2,499 · 3–9 days.
Custom MVP: $8,000–$25,000 · 4–10 weeks depending on scope.
Ongoing retainer: from $2,299/month for feature work and maintenance.
Every quote is written before payment — no surprise invoices after kickoff.

Client Reference

What a real AI agent deployment looks like at Miracuves

A US-based eCommerce company needed an intelligent customer support system to handle 15,000+ monthly support tickets across email, chat, and phone — with 85% first-response automation and seamless human escalation.

01

The Challenge

Existing support team of 12 agents could not scale with growth. Average first response time was 4+ hours. Customer satisfaction scores were dropping. The client needed automation that actually understood context — not keyword-based chatbots.

02

What Miracuves Built

Deployed a multi-agent support system using LangGraph with three specialized agents: triage agent for intent classification, search agent for RAG-powered knowledge base retrieval, and escalation agent for human handoff. Built with 400+ product-specific knowledge documents indexed in Pinecone.

03

Outcome

85% of first-contact issues resolved without human intervention. Average first response time dropped from 4+ hours to 12 seconds. Human agents handle only 15% of tickets — the complex cases that require empathy and judgment. Customer satisfaction improved by 40%.

8 WeeksFull deployment
85%Auto-resolution rate
100%Source owned
View All Case Studies →

Client Testimonial

"We were drowning in support tickets. Every new hire meant weeks of training and they still struggled. Miracuves deployed a multi-agent support system that handled 85% of our volume from day one — order status, returns, shipping questions — all automated. The human escalation flow is seamless. Our support team now focuses on what matters: complex customer situations."

JD

J.D., Head of Customer Experience

US eCommerce Platform · 15K Monthly Tickets

⭐ Read Clutch & Google Reviews →

Project Brief

Solution builtMulti-Agent Support System
Delivery timeline8 weeks
FrameworkLangGraph + Pinecone + FastAPI
Key integrationsZendesk · Slack · Email · Chat
Knowledge base400+ documents indexed
Source code100% client-owned
85%
Auto-resolution rate
12s
Avg first response
40%
CSAT improvement
Client Reviews

What clients say about Miracuves AI agent development

Across eCommerce support, sales automation, and data analytics projects — from mid-market to enterprise — verified on Clutch and Google.

★★★★★

Clutch · Customer Support Automation

"We deployed Miracuves' multi-agent support system across our eCommerce platform. The triage agent correctly classifies 97% of incoming tickets. Search agent pulls answers from 400+ knowledge docs in under 2 seconds. Escalation to humans is seamless. Customer satisfaction up 40% in two months."

JD

J.D., Head of CX

US eCommerce Platform

LangGraph · Multi-Agent · Pinecone · Zendesk
★★★★★

Google Reviews · Sales Automation

"Our sales team was spending 60% of their time on lead qualification. Miracuves built a sales outreach agent that researches prospects, personalizes emails, and books meetings automatically. Our conversion rate improved 35% in the first quarter. The agent handles 500+ outreach sequences daily without errors."

MK

M.K., VP of Sales

B2B SaaS · Series A

AutoGen · CRM Integration · Email Automation
★★★★★

Clutch · Data Analytics Platform

"Miracuves built a data analyst agent that connects to our Snowflake warehouse and generates weekly business reports automatically. Our BI team went from 15 hours per report to 15 minutes of review. The agent handles natural language queries — our product managers ask questions in plain English and get charts back."

PL

P.L., Director of Data

Fintech Analytics · Series B

LangChain · FastAPI · Snowflake · Grafana
4.9 / 5.0 Clutch average rating
4.8 / 5.0 Google average rating
Top Developer Clutch recognition · 2024-2025
Read All Reviews →
Frequently Asked

Questions about AI agent development at Miracuves

What is the typical timeline for building an AI agent system?

A scoped single-agent deployment ships in 4-6 weeks: specification, architecture, tool integration, testing, and deployment. Multi-agent systems with orchestrators take 8-12 weeks. All timelines are stated in writing before any payment is requested.

Can AI agents integrate with my existing tools and APIs?

Yes. Miracuves builds custom tool integrations for any REST, GraphQL, or gRPC API. Standard connectors include Zendesk, Salesforce, HubSpot, Slack, Shopify, Snowflake, PostgreSQL, and custom internal tools. We add new tools as part of every agent delivery.

How do you ensure AI agents don't hallucinate or make mistakes?

Every agent uses structured verification: tool outputs are validated against schemas before being used, all critical actions require human-in-the-loop approval, and we enforce confidence thresholds. If an agent's confidence drops below 85%, it escalates to a human. RAG-based knowledge retrieval grounds every response in verified sources.

What LLM models do your agents use?

Agents support any LLM provider: OpenAI (GPT-4o, GPT-4-turbo), Anthropic (Claude 3.5 Sonnet), and open-source models via self-hosted endpoints. Miracuves recommends the model based on your latency, cost, and accuracy requirements — not a single default.

Can I switch between different LLM providers?

Yes. Miracuves builds with LangChain's model abstraction layer, so switching between OpenAI, Anthropic, or local models requires a configuration change — not a code rewrite. We benchmark and recommend the optimal model for your specific agent workload.

How does Miracuves handle data privacy and security for agents?

All agent data is encrypted at rest and in transit. Miracuves signs a bilateral NDA before any project details are shared. Agents can be deployed in your own VPC or cloud account. No training data leaves your environment. An IP assignment agreement confirming 100% ownership is signed at project start.

What happens when the LLM provider changes pricing or deprecates a model?

Miracuves monitors LLM provider changes and proactively migrates agents to equivalent or better models. The abstraction layer means swapping models does not require re-architecting your agent. Migration support is included in the 60-day post-deployment support window.

Do you provide ongoing support after the agent is deployed?

Every Miracuves delivery includes 60 days of post-launch technical support. Monitoring dashboards (Grafana + Prometheus) track agent accuracy, latency, and error rates. Bugs within scope are fixed at no cost. Monthly maintenance retainers are available at published rates for model updates, tool additions, and performance tuning.

Get Started

Ready to build your AI agent with Miracuves?

Tell Miracuves what you are building. We will confirm the right solution base, service model, and delivery timeline — in writing, before any commitment is required from you.

9,000+Projects delivered
3–9 DaysMVP delivery
100%Source code yours
Same DayNDA turnaround
WhatsApp — Start Now Contact & Brief Form

NDA signed before we discuss your project details

Page reviewed by the Miracuves AI Agent Development Team · Last updated May 2026 · Clutch & Google Reviews