AI agent development is the process of building autonomous software systems that can reason, plan, make decisions, and execute multi-step tasks on behalf of users without constant human supervision. Unlike traditional AI chatbots that only respond to prompts, AI agents independently observe their environment, set goals, use tools (APIs, databases, web browsers), and take actions to accomplish complex objectives. In 2026, AI agents are transforming customer support, sales, healthcare, finance, and operations across every industry. M TECHUB LLC has built AI agent systems including Fate (agentic AI dating with personality interviews and conversation coaching) and AI Health Assistant (multi-specialty AI doctor consultations). The focus on AI agent development will continue to grow as technology evolves.
Table of Contents
- 1. What Is an AI Agent?
- 2. AI Agent vs AI Chatbot: Key Differences
- 3. Types of AI Agents
- 4. How AI Agents Work (Architecture)
- 5. Real-World AI Agent Use Cases by Industry
- 6. AI Agent Development Cost
- 7. AI Agent Development Timeline
- 8. AI Agent Technology Stack
- 9. How M TECHUB LLC Builds AI Agents
- 10. Real AI Agent Projects by M TECHUB LLC
- 11. When Your Business Needs an AI Agent
- 12. Common Mistakes in AI Agent Development
- 13. AI Agent Development Checklist
- 14. FAQs
1. What Is an AI Agent?
An AI agent is an autonomous software system powered by large language models (LLMs) that can perceive its environment, reason about goals, plan multi-step actions, use external tools, and execute tasks independently. The key word is autonomous. An AI agent does not wait for a human to tell it what to do at every step. You give it a goal, and it figures out how to achieve that goal on its own.
Think of the difference between a calculator and an accountant. A calculator (traditional AI chatbot) only does exactly what you tell it, one calculation at a time. An accountant (AI agent) understands your financial goals, gathers data from multiple sources, analyses patterns, makes recommendations, and takes actions like filing reports or flagging anomalies without you directing each step.
The Four Capabilities That Define an AI Agent
| Capability | What It Means | Example |
| Perception | Observes and processes information from its environment | Reads emails, monitors databases, scans documents, listens to voice input |
| Reasoning | Analyses information and makes logical decisions | Evaluates whether a customer query needs escalation or can be resolved autonomously |
| Planning | Creates multi-step action plans to achieve goals | Maps out a sequence: search database, draft response, check compliance, send email |
| Action | Executes tasks using tools, APIs, and integrations | Calls an API, sends an email, updates a CRM record, schedules a meeting, generates a report |
Simple definition: An AI agent is software that thinks, plans, and acts. A chatbot is software that responds.
2. AI Agent vs AI Chatbot: Key Differences
This is the most common confusion in 2026. Many businesses think they need an AI chatbot when they actually need an AI agent, or vice versa. Here is the clear distinction:
| Factor | AI Chatbot | AI Agent |
| Interaction Model | Responds to user prompts one at a time | Autonomously plans and executes multi-step tasks |
| Memory | Limited context (within single conversation) | Persistent memory across sessions and interactions |
| Tool Usage | None or minimal (text generation only) | Uses APIs, databases, browsers, calendars, CRMs, email |
| Decision Making | Follows scripted flows or generates responses | Reasons about goals, evaluates options, makes autonomous decisions |
| Goal Orientation | Answers the current question | Works toward achieving a defined objective over multiple steps |
| Proactivity | Reactive only (waits for user input) | Proactive (can initiate actions, send alerts, monitor events) |
| Example | ChatGPT answering a question | An AI agent that monitors your inbox, drafts responses, schedules meetings, and updates your CRM automatically |
| Complexity | Low to medium | Medium to high |
| Cost to Build | $10,000 – $40,000 | $40,000 – $200,000+ |
| Best For | Customer FAQ, simple support, content generation | Sales automation, operations, healthcare, complex workflows |
When to choose a chatbot: You need to answer common questions, provide basic customer support, or generate content. The interaction is simple: user asks, AI responds.
When to choose an AI agent: You need to automate complex workflows, make autonomous decisions, use multiple tools and data sources, and take actions without human approval at every step.
Real example: M TECHUB LLC built Fate as an agentic AI system, not a chatbot. The AI agent conducts personality interviews, analyses communication patterns across multiple interactions, curates compatible matches based on accumulated personality data, and coaches users through conversations with real-time contextual prompts. A chatbot could answer dating questions. An agent actively manages the entire matchmaking process.
3. Types of AI Agents
AI agents range from simple to highly complex. Understanding the types helps you determine what level of agent your business needs:
3.1 Simple Reflex Agents
React to current input based on predefined rules. No memory, no planning. Example: an email autoresponder that sends a template reply when it detects certain keywords.
Cost range: $5,000 – $15,000. Best for simple automation tasks.
3.2 Model-Based Agents
Maintain an internal model of the world and use it to make decisions. They have memory and can track state changes over time. Example: a customer support agent that remembers previous interactions and adjusts responses accordingly.
Cost range: $15,000 – $50,000. Best for support and service automation.
3.3 Goal-Based Agents
Work toward specific objectives by evaluating different action sequences and choosing the best path to achieve the goal. Example: a sales agent that identifies prospects, qualifies leads, personalises outreach, and schedules meetings to hit a quarterly revenue target.
Cost range: $40,000 – $100,000. Best for sales, marketing, and operations.
3.4 Utility-Based Agents
Optimise decisions based on a utility function that weighs multiple factors. They choose actions that maximise overall value, not just achieve a single goal. Example: a portfolio management agent that balances risk, return, and diversification across multiple investment strategies.
Cost range: $80,000 – $200,000. Best for finance, trading, and complex optimisation.
3.5 Multi-Agent Systems
Multiple specialised agents working together, each handling a different aspect of a complex workflow. They communicate, coordinate, and delegate tasks among themselves. Example: a customer service system with separate agents for triage, technical support, billing, and escalation.
Cost range: $100,000 – $300,000+. Best for enterprise operations and complex platforms.
Real example: M TECHUB LLC built the AI Health Assistant with 8+ specialised AI agents, each trained for a different medical specialty (Cardiologist, Oncologist, Gynecologist, Orthopedist, Nephrologist, Radiologist, General Physician). Each agent handles consultations within its specialty domain, and the system routes users to the appropriate specialist agent based on their symptoms and queries.
4. How AI Agents Work: The Architecture
Every AI agent, regardless of complexity, follows this core architecture:
4.1 The Agent Loop
AI agents operate in a continuous loop: Perceive (gather information from environment and tools), Reason (analyse the information using an LLM), Plan (determine the next action or sequence of actions), Act (execute the action using available tools), and Observe (evaluate the result and decide whether the goal is achieved or more steps are needed). This loop repeats until the goal is accomplished or the agent determines it cannot proceed without human intervention.
4.2 Core Components
| Component | Purpose | Technologies |
| LLM Brain | Reasoning, planning, and decision-making | OpenAI GPT-4o, Google Gemini, Anthropic Claude, Meta Llama |
| Memory System | Short-term (conversation) and long-term (persistent knowledge) | pgvector, Pinecone, Weaviate, Redis, ChromaDB |
| Tool Kit | APIs and integrations the agent can use to take actions | REST APIs, function calling, MCP servers, browser automation |
| Orchestration Layer | Manages the agent loop, tool routing, and error handling | LangChain, LangGraph, CrewAI, AutoGen, custom orchestration |
| Guardrails | Safety constraints preventing harmful or incorrect actions | Input validation, output filtering, human-in-the-loop approval |
| Retrieval System (RAG) | Grounds the agent in your specific business data | RAG pipelines with vector databases and document embeddings |
4.3 Tool Calling: How Agents Take Action
The most important capability of an AI agent is tool calling (also called function calling). This is how the LLM brain connects to the real world. When an agent decides it needs to take an action, it generates a structured tool call that your system executes. For example, an agent might call a CRM API to update a lead status, a calendar API to schedule a meeting, an email API to send a follow-up, or a database query to retrieve customer history.
Technologies: OpenAI function calling, Google Gemini tool use, Anthropic Claude tool use, and Model Context Protocol (MCP) for standardised tool integration.
4.4 RAG: Grounding Agents in Your Data
RAG (Retrieval-Augmented Generation) is how you make an AI agent knowledgeable about your specific business. Instead of relying solely on the LLM training data, RAG retrieves relevant documents from your knowledge base, product catalog, policy documents, or customer history and injects them into the agent context before it reasons and responds.
Technologies: pgvector (PostgreSQL extension), Pinecone, Weaviate, ChromaDB for vector storage. LangChain and LlamaIndex for RAG pipeline orchestration.
5. Real-World AI Agent Use Cases by Industry
AI agents are not theoretical. They are in production right now across every major industry:
| Industry | AI Agent Use Case | What the Agent Does | Business Impact |
| Customer Support | Autonomous Support Agent | Resolves 70-80% of tickets without human intervention, escalates complex issues | Reduces support costs by 40-60% |
| Sales | AI Sales Development Rep | Identifies prospects, personalises outreach, qualifies leads, schedules meetings | Increases pipeline by 2-3x |
| Healthcare | AI Medical Consultant | Conducts specialty consultations, analyses medical reports, tracks vitals | Reduces patient wait times from days to seconds |
| Finance | AI Portfolio Manager | Monitors markets, analyses fundamentals, recommends trades, manages risk | Processes data 1000x faster than human analysts |
| Legal | AI Legal Research Agent | Reviews contracts, identifies risks, researches case law, drafts summaries | Reduces review time by 80% |
| HR | AI Recruitment Agent | Screens resumes, matches candidates, schedules interviews, sends follow-ups | Reduces time-to-hire by 50% |
| E-Commerce | AI Shopping Assistant | Recommends products, answers questions, processes returns, upsells | Increases conversion by 15-25% |
| Real Estate | AI Property Agent | Matches listings to buyer preferences, schedules viewings, generates reports | Handles 10x more leads simultaneously |
| Education | AI Tutoring Agent | Adapts to student learning style, provides personalised exercises, tracks progress | Improves completion rates by 30-40% |
| Operations | AI Workflow Agent | Monitors processes, detects anomalies, triggers corrective actions, generates reports | Reduces manual oversight by 60% |
6. AI Agent Development Cost
AI agent development cost depends on agent complexity, number of tools and integrations, data requirements, and the level of autonomy required:
| Agent Type | Complexity | Cost Range | Timeline | Example |
| Simple Reflex Agent | Low | $5,000 – $15,000 | 2 – 4 weeks | Email autoresponder, form processor |
| Support Chatbot with RAG | Low-Medium | $15,000 – $40,000 | 4 – 8 weeks | Customer FAQ bot with company knowledge |
| Goal-Based Single Agent | Medium | $40,000 – $100,000 | 2 – 4 months | Sales SDR agent, booking agent, research agent |
| Multi-Tool Agent | Medium-High | $80,000 – $150,000 | 3 – 6 months | Operations agent with CRM, email, calendar, database access |
| Multi-Agent System | High | $100,000 – $250,000 | 4 – 8 months | Enterprise support system with specialised agent teams |
| Custom Agentic Platform | Very High | $200,000 – $500,000+ | 6 – 12+ month+ | Full agentic AI product like Fate or AI Health Assistant |
What drives AI agent cost up: Number of tools the agent uses (each integration costs $2,000-$10,000), complexity of reasoning required, amount of training data and RAG setup, custom guardrails and compliance requirements, real-time performance needs, and multi-agent coordination logic.
What keeps cost down: Using managed LLM APIs (OpenAI, Gemini, Claude) instead of self-hosted models, leveraging existing frameworks (LangChain, CrewAI), starting with a single-agent MVP before building multi-agent systems, and using pre-built tool integrations.
7. AI Agent Development Timeline
| Phase | Simple Agent | Complex Agent | Multi-Agent System |
| Discovery and data assessment | 1 week | 2 – 3 weeks | 3 – 4 weeks |
| Agent architecture design | 1 week | 2 – 3 weeks | 3 – 5 weeks |
| RAG pipeline and data setup | 1 – 2 weeks | 3 – 5 weeks | 4 – 8 weeks |
| Agent development and tool integration | 2 – 4 weeks | 6 – 12 weeks | 10 – 20 weeks |
| Testing, guardrails, and safety | 1 – 2 weeks | 3 – 5 weeks | 5 – 8 weeks |
| Deployment and monitoring | 1 week | 1 – 2 weeks | 2 – 3 weeks |
| TOTAL | 6 – 10 weeks | 4 – 8 months | 6 – 12+ months |
8. AI Agent Technology Stack
Here is the technology stack M TECHUB LLC uses for AI agent development:
| Layer | Technologies | Purpose |
| LLM Providers | OpenAI GPT-4o, Google Gemini, Anthropic Claude, Meta Llama | Agent reasoning and decision-making |
| Orchestration | LangChain, LangGraph, CrewAI, AutoGen, custom Python | Agent loop, tool routing, multi-agent coordination |
| Vector Database | pgvector, Pinecone, Weaviate, ChromaDB | RAG knowledge storage and retrieval |
| Backend | Node.js, Python (FastAPI), Go | API server, agent hosting, tool execution |
| Database | PostgreSQL, MongoDB, Redis | Persistent storage, caching, session management |
| Frontend | React, Next.js, React Native | User interfaces for agent interactions |
| Cloud | AWS (SageMaker, Lambda, Bedrock), Google Cloud (Vertex AI) | Model hosting, serverless execution, scaling |
| Monitoring | LangSmith, Helicone, custom dashboards | Agent performance, cost tracking, quality metrics |
External reference: OpenAI function calling documentation is the foundation for most agent tool integrations. LangChain documentation provides the most comprehensive agent orchestration framework.
9. How M TECHUB LLC Builds AI Agents
M TECHUB LLC follows a structured 6-phase process for AI agent development, refined through delivering 700+ products including multiple AI-powered platforms:
Phase 1: Discovery and Use Case Definition
We start by understanding your business workflow, identifying which tasks can be automated by an AI agent, and defining the agent scope. We map out the tools the agent needs access to, the data sources it will draw from, and the guardrails required for safe autonomous operation.
Phase 2: Data Assessment and RAG Setup
We evaluate your existing data, clean and structure it for RAG pipelines, set up vector databases, and create the knowledge base that grounds your agent in your specific business context. Without this step, your agent is just a generic LLM with no domain expertise.
Phase 3: Agent Architecture Design
We design the agent architecture including LLM selection, tool definitions, orchestration logic, memory systems, and guardrails. For multi-agent systems, we define agent roles, communication protocols, and delegation logic.
Phase 4: Development and Tool Integration
We build the agent using LangChain, LangGraph, or custom orchestration, integrate all required tools via function calling, implement the RAG pipeline, and create the user interface for agent interactions. Development follows agile sprints with demos every 2 weeks.
Phase 5: Testing, Safety, and Guardrails
We test the agent across hundreds of scenarios including edge cases, adversarial inputs, and failure modes. We implement guardrails for harmful outputs, data privacy, cost controls (preventing runaway API usage), and human-in-the-loop approval for high-stakes actions.
Phase 6: Deployment and Continuous Optimisation
We deploy the agent to production with monitoring dashboards tracking response quality, tool usage, cost per interaction, and user satisfaction. We provide 3-6 months of free post-launch support including prompt tuning, RAG optimisation, and model upgrades.
10. Real AI Agent Projects by M TECHUB LLC
Fate: Agentic AI Dating Platform
M TECHUB LLC built Fate, the world first agentic AI dating app featured in The Guardian. The AI agent conducts personality interviews, analyses communication style across multiple interactions, curates 5 deeply compatible matches per cycle based on accumulated personality data, and coaches users through conversations with real-time contextual prompts. Unlike chatbot-based dating apps, Fate AI agent actively manages the entire matchmaking journey from interview through match delivery to conversation coaching. Result: 50,000+ downloads in 60 days.
AI Health Assistant: Multi-Specialty AI Doctor
M TECHUB LLC built an AI health consultation platform with 8+ specialised AI agents, each trained for a different medical specialty. The system routes patients to the appropriate specialist agent based on symptoms, conducts consultations, analyses uploaded medical reports using OCR, and tracks health vitals over time. Each specialty agent maintains its own knowledge base and consultation protocols. Result: 100,000+ downloads, 4.2-star rating, under 5-second response time.
Savage Mushroom Fitness: AI Meal Planning Agent
M TECHUB LLC built an AI agent within Savage Mushroom Fitness that uses Google Gemini with tool-calling architecture to generate personalised weekly meal plans. The agent accesses user profile data (67 data points), calculates deterministic calorie targets, generates meal options, and sources food images through a 3-tier strategy (Spoonacular, Edamam, Pexels). The agent adapts plans as users log progress and adjust goals. Result: 94% onboarding completion, 4.8/5 meal plan accuracy vs nutritionist benchmarks.
OnSkin: AI Skincare Analysis Agent
M TECHUB LLC built an AI agent for OnSkin that analyses skincare products through multiple input methods (barcode scan, photo recognition, text search), scores ingredient safety against clinical databases (IARC, California EPA, European Commission), and recommends safer alternatives. The agent processes 2 million+ products and serves 8 million+ users. Result: Double Webby Award 2024, featured in Forbes, CNN, and Vogue.
11. When Your Business Needs an AI Agent (Not Just a Chatbot)
You need an AI agent when:
- Your team spends more than 20 hours per week on repetitive tasks that follow patterns
- Your workflow requires pulling data from 3+ different systems before making a decision
- Your customers wait hours or days for responses that could be instant
- You need 24/7 availability but cannot staff around the clock
- Your current chatbot handles less than 30% of queries successfully
- You want to scale operations without proportionally scaling headcount
- Your competitors are already using AI agents and gaining efficiency advantages
- You have structured data (CRM, ERP, documents) that an agent could leverage for better decisions
You do NOT need an AI agent when:
- Your support queries are simple FAQ-style questions (a chatbot with RAG is sufficient)
- You do not have structured data for the agent to work with
- Your workflow changes constantly and cannot be systematised
- Your industry has strict regulations that prevent autonomous AI decision-making
12. Common Mistakes in AI Agent Development
Mistake 1: Building a multi-agent system when a single agent would suffice
Multi-agent systems are 3-5x more expensive and complex. Start with a single goal-based agent that handles your highest-value workflow. Add more agents only when the single agent reaches its limitations. M TECHUB LLC always recommends the simplest architecture that solves the problem.
Mistake 2: Skipping RAG and relying solely on the LLM knowledge
Without RAG, your agent only knows what GPT or Gemini learned during training. It knows nothing about your products, policies, customers, or internal processes. RAG is what makes a generic AI into YOUR AI agent. Budget $5,000-$15,000 for proper RAG setup.
Mistake 3: No guardrails or safety constraints
An AI agent without guardrails will eventually do something wrong, whether that is sending an inappropriate email, sharing confidential data, or making an incorrect decision. Every production agent needs input validation, output filtering, action approval workflows for high-stakes decisions, and cost controls to prevent runaway API spending.
Mistake 4: Ignoring monitoring and observability
You cannot improve what you do not measure. Every AI agent needs monitoring for response quality, tool usage patterns, cost per interaction, error rates, and user satisfaction. Tools like LangSmith and Helicone make this straightforward. Budget 5-10% of agent development cost for monitoring setup.
Mistake 5: Choosing the wrong LLM provider
Not every task needs GPT-4o. Simple agents work fine with GPT-4o-mini or Claude Haiku at a fraction of the cost. Complex reasoning tasks may need Claude Opus or Gemini Pro. M TECHUB LLC evaluates multiple providers during the architecture phase and selects the best cost-performance balance for your specific use case.
13. AI Agent Development Checklist
Use this checklist before starting your AI agent project:
Planning
☐ Clearly defined use case with measurable success metrics
☐ Identified which tasks the agent will automate
☐ Listed all tools and APIs the agent needs access to
☐ Mapped data sources for RAG knowledge base
☐ Defined guardrails and safety constraints
☐ Determined human-in-the-loop requirements for high-stakes actions
Technical
☐ Selected LLM provider based on cost-performance evaluation
☐ Designed agent architecture (single vs multi-agent)
☐ Set up vector database for RAG pipeline
☐ Implemented tool calling with proper error handling
☐ Built monitoring and observability dashboard
☐ Tested across 100+ scenarios including edge cases and adversarial inputs
Business
☐ Calculated ROI based on time saved and cost reduced
☐ Defined escalation path when agent cannot resolve a task
☐ Planned for post-launch optimisation (prompt tuning, RAG updates)
☐ Budgeted for ongoing LLM API costs (scales with usage)
☐ Secured 3-6 months post-launch support from development partner
Ready to build an AI agent? M TECHUB LLC has delivered agentic AI systems for Fate (Guardian feature), AI Health Assistant (100K+ downloads), Savage Mushroom Fitness (Gemini tool-calling), and OnSkin (8M+ users). Free AI consultation at project@mtechub.com | https://mtechub.com/contact
14. Frequently Asked Questions
What is AI agent development?
AI agent development is the process of building autonomous software systems that can reason, plan, and execute multi-step tasks using large language models, tool calling, and business data. Unlike chatbots that only respond to prompts, AI agents independently make decisions and take actions to achieve defined goals. M TECHUB LLC provides AI agent development services through our AI Development practice.
How much does AI agent development cost?
AI agent development costs range from $5,000 for a simple reflex agent to $500,000+ for a complex multi-agent enterprise platform. A typical goal-based single agent with RAG and 3-5 tool integrations costs $40,000-$100,000 and takes 2-4 months. M TECHUB LLC provides detailed estimates after a free discovery call.
What is the difference between an AI agent and an AI chatbot?
An AI chatbot responds to user prompts one at a time with no autonomy or tool usage. An AI agent independently reasons, plans, uses tools (APIs, databases, email), and executes multi-step tasks to achieve goals. Chatbots are reactive. Agents are proactive and autonomous. See the detailed comparison table in Section 2 of this guide.
What technologies are used to build AI agents?
Core technologies include LLM providers (OpenAI GPT, Google Gemini, Anthropic Claude), orchestration frameworks (LangChain, LangGraph, CrewAI), vector databases for RAG (pgvector, Pinecone, Weaviate), backend frameworks (Node.js, Python FastAPI), and cloud infrastructure (AWS, Google Cloud). See Section 8 for the complete technology stack.
How long does it take to build an AI agent?
A simple agent takes 6-10 weeks. A complex goal-based agent takes 2-4 months. A multi-agent enterprise system takes 4-8 months. Timelines depend on the number of tool integrations, RAG complexity, and testing requirements. M TECHUB LLC follows agile development with 2-week sprint demos.
Can AI agents replace human employees?
AI agents augment human employees, not replace them. They handle repetitive, data-heavy tasks at scale and speed that humans cannot match, freeing human workers to focus on creative, strategic, and relationship-driven work. The best implementations combine AI agent automation with human oversight for high-stakes decisions.
What industries benefit most from AI agents?
Customer support, sales, healthcare, finance, legal, HR, e-commerce, real estate, education, and operations all benefit significantly from AI agents. Any industry with repetitive workflows, large data volumes, and the need for 24/7 availability is a strong candidate. See Section 5 for detailed industry use cases.
Can M TECHUB LLC build an AI agent for my business?
Yes. M TECHUB LLC provides end-to-end AI agent development including discovery, architecture design, RAG setup, agent development, tool integration, testing, deployment, and 3-6 months free post-launch support. We have built agentic AI systems for dating (Fate), healthcare (AI Health Assistant), fitness (Savage Mushroom), and skincare (OnSkin). Contact us at info@mtechub.com or visit https://mtechub.com/contact for a free consultation.
Is AI agent development worth the investment?
For most businesses, yes. A well-built AI agent that automates a 40-hour-per-week workflow at $40,000 development cost pays for itself in 3-6 months through labour savings alone. Additional benefits include 24/7 availability, consistent quality, scalability without proportional headcount growth, and faster response times. M TECHUB LLC helps calculate ROI during the discovery phase before you commit to development.
Related Resources from M TECHUB LLC
- AI Development Services
- Mobile App Development Services
- SaaS Development Services
- MVP Development Services
- Software Development Case Studies
- Contact M TECHUB LLC
External References
- OpenAI Function Calling Documentation
- LangChain Documentation
- Google Gemini AI
- Anthropic Claude
- Pinecone Vector Database
Ready to Build an AI Agent for Your Business?
M TECHUB LLC builds AI agents that think, plan, and act for your business. From simple support agents to complex multi-agent enterprise systems, we handle the entire process from discovery through deployment with 3-6 months free post-launch support. 700+ products delivered. Clutch and DesignRush top-rated. Global Excellence Award USA 2024-25.
About the Author
Subtain Afzal is the Co-Founder and CTO of M TECHUB LLC, a global software development company headquartered in Sterling, Virginia with offices in London, Dubai, and Islamabad. With 700+ products delivered across 35+ countries, Subtain leads a team of 200+ engineers specialising in AI agent development, mobile apps, SaaS platforms, and enterprise software. M TECHUB LLC is Clutch and DesignRush top-rated and holds the Global Excellence Award USA 2024-25.
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