AI Agents Development for Business Processes Automation: from Concept to Deployment

Companies today grapple with market saturation, aggressive timelines, and calls for innovation at scale. Many consider the Agentic AI an actionable solution to these challenges, that will help optimize operating costs, automate key processes, and retain high performance levels.

Yet, companies often struggle to understand where to begin with AI Agent Development. They face an AI skills gap, which makes integrating agents into their existing systems difficult. Furthermore, the uncertainty about the selection of architecture or how to go about flawless system integration can slow things down.

At MobiDev, we started building AI agents years before the term became a buzzword. More than a decade of experience in custom software development and a growing expertise in AI has allowed us to provide our clients with intelligent systems that automate processes, enhance decision-making, and improve customer interactions across industries.

In this article, I will summarize our practical experience and provide a step-by-step roadmap on how to create an AI agent, addressing top challenges for different businesses aiming to incorporate this technology in their software products, both internal and commercial.

Key 5 Strategic Advantages of AI Agents

The latest McKinsey Global Survey of AI reports that 65% of respondents say their organizations employ generative AI on a regular basis, almost twice the proportion of ten months ago. A Gartner report also discovers that 58% of finance functions are expected to utilize AI technologies in 2024, a 21-point rise from last year. These statistics show that the adoption of AI is no longer an option—companies that leverage AI agents stand a greater chance of staying competitive in a rapidly evolving market.

AI agents are now a necessity for businesses that must optimize performance, remain agile, and future-proof their systems. The following are the primary benefits of incorporating AI agents in your software system.

Enhance Software Performance Efficiency

Introducing AI into your software adds automated workflows, advanced analytics, and real-time data processing. By using agentic AI, you can offload tasks such as round-the-clock customer support and data-driven decision-making. AI agents provide quick, reliable responses without human intervention, minimizing errors and ensuring users receive accurate information at any hour. This reliability drives faster issue resolution and leads to higher satisfaction across your user base.

Utilize Scalability Features

Many software businesses, SaaS especially, scale rapidly, which can bog down traditional customer support or analytics software. AI agents can be configured to handle rising volumes of requests, with consistent service levels. If your user base doubles overnight, your AI agents can handle heightened interactions without risking performance degradation or system lag.

Gain Customer Insights

Every exchange with an AI agent generates data on user behaviors and preferences. With the right analytics, this data opens up trends in user engagement, pervasive pain points, and emerging market needs. With these insights in hand, you can tune your offerings, make customer experiences more personal, and even predict user needs. The result is a more personalized software tool that evolves in tandem with market evolution.

Gain Competitive Advantages

With the addition of sophisticated automation and data intelligence, your software solution gains a distinctive market differentiation. Some of the features distinguishing you include intelligent recommendations, anomaly detection in real-time, and context-aware user guidance. Such differentiation has the effect of leading to a larger customer base, enhanced brand loyalty, and the potential for accelerated revenue growth.

Decrease Churn and Boost CLV

The enhanced performance and personalized service typically equate to better customer retention, lowering churn rates. Since retention directly impacts recurring revenue, this also boosts your Customer Lifetime Value (CLV). Since users enjoy the efficiency gains your AI agent delivers, they’ll be more likely to remain loyal and adopt new features you launch, promoting long-term profitability.

When to Use AI Agents

AI agents are best suited for open-ended problems where it is either hard or not feasible to foresee the number of steps precisely, and a pre-defined, hardcoded sequence is not feasible. These agents utilize large language models (LLMs) to operate across multiple interactions, where some level of trust in their decision-making is required. It is their autonomy that makes them extremely handy for task scaling in controlled and trusted environments.

However, this autonomy comes with a rise in costs along with the possibility of compounding errors. To overcome these challenges, we recommend thorough testing in sandbox environments, along with implementing appropriate guardrails to attain reliability and safety.

7 Steps of AI Agent Development Process: Roadmap for Software Companies

What follows is a structured process to guide on how to create an AI agent, from strategy to post-deployment maintenance.

1. Define the Business Objectives and Determine AI Strategy

A good strategy underpins AI agent development. Clearly define the specific problems your solution will solve, such as automating routine tasks or personalizing user experiences. Ask whether you actually need advanced machine learning or whether a simpler rule-based system will suffice.

Also, identify key performance indicators (KPIs). Perhaps you wish to reduce support costs, enable real-time data analysis, or improve customer retention. With your goals defined, you can chart AI complexity and resources accordingly.

2. Gather a Development Team

Next, determine whether you have the necessary in-house expertise for AI agent development. To build an AI agent that functions in unison, you’ll need a team that includes:

• AI engineer
• software developers
• UI/UX experts

If your in-house team is lacking, you can outsource. You can fill gaps through external AI consulting, a Dedicated Development team, or Team augmentation. For instance, even software market leaders sometimes require extra ML support in order to get new AI-powered features to market on time.

3. Collect and Preprocess Training Data (if applicable)

Quality data is the lifeblood of the AI agent development process. If the AI agent is not based on an LLM, ensure your data sources—whether logs, user interactions, or third-party APIs—are relevant and accurate. However, if the AI agent operates on an LLM, this step is not required, as the model is not trained with collected data. In this case, you will also need to pay special attention to developing additional tools that ensure a seamless connection to your existing systems for AI agents.

Consider privacy regulations like GDPR or HIPAA if your solution deals with sensitive data. A healthcare solution, for example, might need HIPAA-compliant storage and aggressive anonymization methods.

If applicable, also determine how frequently you will update the data used to train your AI model. Stale data causes performance degradation over time, resulting in inaccurate predictions and diminished user experiences.

4. Choose the Technology & Tools

Select your technology stack carefully. Some companies opt to create AI agents from scratch using frameworks such as Microsoft Autogen, LangChain, or LlamaIndex, alongside libraries like PyTorch, TensorFlow, or scikit-learn for specialized tasks.

To reduce development overhead, businesses can also utilize AI agent platforms that offer solutions for integration, hosting, and scaling of your created agents. Here are my top picks for the best AI agent platforms:

• Gumloop
• Relay.app
• HockeyStack
• Stack AI
• Voiceflow
• Flowise

For larger systems, cloud providers such as AWS, Azure, or Google Cloud AI offer built-in solutions that simplify creating AI agents at scale. The decision between on-premises versus cloud depends on data security needs, latency requirements, and operational budgets. If you’re unsure, consider tech strategy consulting for personalized recommendations.

For some narrowly specialized tasks, utilizing Small Language Models (SLM) instead of Large Language Models (LLM) can significantly decrease the complexity, duration, and cost of your AI agent development.

5. Build the AI Agent

This stage unites a number of efforts—algorithm training, data processing, and software engineering. ML engineers refine algorithms and hyperparameters to avoid underfitting or overfitting. Software developers integrate the AI functionality into your IT environment, exposing the agent via APIs or embedded microservices.

If your AI is conversational in nature, you can also develop user-facing UIs as in chatbot development. Keep in mind that successful development of an AI Agent is a team effort: the ML pipeline, UX design, and system architecture must evolve in tandem.

6. Evaluate the Performance of the AI Agent

Subject your AI to a controlled environment and challenge it with real-world situations. Make sure the AI system can handle edge cases or strange user inputs without breaking. Typical testing methods range from A/B testing to user experience testing.

With good strategies for testing AI agents, you catch errors before subjecting them to end users. Test for accuracy, latency, and scalability. Test for compliance with service-level agreements (SLAs). A well-designed test phase ensures confidence when moving from pilot to production.

7. Deploy the AI Agent in Your Product & Maintain

Finally, deploy the trained AI in your IT environment. You can leverage CI/CD pipelines to automate the updates and roll out the improvements at scale. Monitor continuously—collect logs, user input, and performance metrics.

This feedback loop enables you to fine-tune the AI system, address any concept drift, and maintain and improve your agent’s performance over time. Think of deployment as a circular and ongoing process: gather real-world outcomes, feed them back into the pipeline, and continually enhance your AI agent to meet changing user needs and environments.

AI Agent Development Challenges & How to Get Past Them

It’s not enough to understand how to build an AI agent. You also need to know the challenges of development and how to avoid them. In this section, I share the difficulties our team faced when building Intelligent agents.

Inadequate Business Use Cases & Unclear Business Goals

Many companies are too eager to hop on the AI trends. As a result, they start development by learning how to build AI agents, rather than carefully analyzing how the technology can align with their business objectives.

When investing in AI agent solutions without identifying measurable objectives, organizations can find themselves committing resources to aspects that have minimal impact.

Solution:

It is necessary for companies to define the specific problems that will be solved by AI and establish how success will be quantified. Feasibility studies and proof of concept (PoC) are processes that ascertain if sophisticated AI is indeed necessary. In some cases, a simpler data-driven solution suffices.

Data Availability & Quality

AI models require a steady stream of clean, labeled data. If your dataset is biased or not consistent, you may end up with inaccurate predictions.

Solution:

Implement strict data preprocessing, and attempt to utilize synthetic data for scenarios in which real-world samples are limited. To guarantee remaining relevant, schedule periodic updates or retraining.

AI Model Selection & Customization

Irrespective of whether you are building an AI agent from scratch or using pre-trained models, alignment with your business needs is the most critical factor. An incorrect choice of architecture will impede performance — or even worse, may lead your AI agent to make false decisions, which can be more damaging than not using an AI agent at all.

Solution:

A phased PoC strategy—where you test multiple algorithms before large-scale rollouts—enables you to determine the best-fit model. Fine-tune or enhance the baseline model if off-the-shelf products don’t cater to your specific demands.

Scalability & Performance Optimization

With more user traffic, your AI must handle more volume without decelerating. Computationally demanding inference will slow down your software tool, creating a bad user experience.

Solution:

Model pruning and quantization methods lower the computational burden. Near real-time latency is provided by edge computing for mission-critical systems. Load balancing across multiple nodes will also stabilize performance under heavy loads.

Integration with Existing Systems

Legacy infrastructures may be complex if you are attempting to figure out how AI agents work and how to seamlessly connect them with older databases and APIs.

Solution:

Modular architectures—often leveraging containerization and microservices—enable you to add or upgrade AI components with less friction. APIs or middleware can normalize data flows between new AI functionality and legacy systems.

Cost & Resource Constraints

It can require a lot of computational power to train an AI model, especially large language models. Meanwhile, Startups and growing SaaS businesses typically have limited budgets.

Solution:

It is prudent to start with APIs and proper AI agent architecture — usually without model training (e.g., RAG). If needed, scale using cloud solutions, and only consider model training or fine-tuning if previous steps don’t meet your needs. This approach helps avoid unnecessary costs.

Outsourcing to an AI agent development company or using AI-as-a-Service (AIaaS) and open-source platforms can also reduce hiring and infrastructure expenses if your team lacks expertise.

Security, Privacy & Compliance

Your AI is capable of processing personal or sensitive data, which needs to be strongly encrypted and safeguarded in storage. Different industries also have strict regulations to follow—healthcare, for instance, AI solutions need to comply with HIPAA.

Solution:

Regular audits and good data governance procedures reduce the danger of breaches and ensure compliance. This diligence is necessary for users’ trust and brand reputation. For a specialized look at chatbots in healthcare, check out our healthcare chatbot development insights

Ethical & Bias Concerns

Since AI models learn from past data, they learn to reproduce existing biases. For instance, if your training sets are biased, your AI agent can produce discriminatory outcomes, or if you use LLM models, their responses can be affected by hallucinations.

Solution:

Fairness testing, the utilization of heterogeneous datasets in the first case, or the use of RAG in the case of LLM, and the implementation of explainable AI (XAI) techniques minimize such issues. The development of transparent AI enhances users’ trust in AI and adherence to upcoming ethical laws.

Continuous Learning & Maintenance

All AI is subject to “model drift” as user behavior evolves or market conditions change. Unless continuously monitored and retrained incrementally, performance deteriorates.

Solution:

Automated feedback loops send new data to your model to ensure it remains up-to-date and accurate. This may include data audits and adjustments to the agent’s approach or architecture if needed. Such a process is especially critical for SaaS platforms that must quickly adapt to evolving user requirements.

Product-Specific Best Practices for Building AI Agents

In my work, I often see that different types of software products face different types of challenges. This section summarizes my experience with four types of projects regarding AI deployment in software products and offers best practices for each use case. These include:

• Legacy software products that want to modernize their solution.
• Software startups that want to provide the market with powerful AI-based tools.
• Mature SaaS companies that want to incorporate AI agents in their products.
• Companies with in-house software that want to boost its performance.

Challenges of Introducing Agentic AI in Legacy Software For Existing Companies

Offering Seamless Integration with Legacy Systems
Monolithic legacy architectures can be less compatible with AI solutions. You also risk data silos unless the AI model has real-time access to customer or operations databases.

Managing AI Adoption Without Disrupting Current Operations
A sudden AI rollout can be disruptive to workflows and lead to employee pushback. Pilots or phased deployments enable you to validate AI effectiveness and calibrate user experience.

Measuring AI Implementation ROI and Effectiveness
AI-driven enhancements do not always translate into revenue jumps. Measurable metrics provide a tangible metric for success.

AI Agents Challenges For Startups Creating AI-Powered Software Solutions

Startups entering the AI-enhanced software industry are faced with unique challenges that impede their progress toward growing, innovating, and competing effectively. The most prominent challenges they must struggle with are enumerated below:

1. Choosing the Proper AI Technology & Architecture

Startups are facing difficulties selecting the most appropriate AI frameworks, models, and infrastructure that are likely to ensure they remain successful over the years to come. The wrong decision creates scalability issues, huge maintenance costs, and technical debt that are likely to require extensive rework at a later point. The fast-paced nature of AI also presents the difficulty of anticipating the most likely successful technologies over the years to come.

2. Balancing Innovation with Cost of Development

AI app development involves extensive investments of computing resources, model training, and infrastructure for the necessary data. Startups must balance the demand for AI frontier push with the limitation of tight budgets. Demonstrating market fit for the product while justifying the cost of AI for the investor presents another challenge.

3. Hiring and Retaining AI Talent

Artificial intelligence developers, machine learning professionals, and data scientists are well-paid and much in demand, making it challenging for startups to build and retain skilled teams within the organization. Competition with tech giants and well-funded companies for the best AI professionals presents another challenge. Startups are generally faced with knowledge gaps, tight budgets for recruitment, and the need for interdisciplinary knowledge for the successful implementation of AI solutions.

Challenges for Mature SaaS Companies Integrating AI Agents

Mature SaaS businesses aim at adding AI-enhanced functionalities within their offerings but are faced with several obstacles that impede adoption, scalability, and user satisfaction. The most important of these are elaborated upon below:

1. Smooth Adoption of AI That Won’t Disrupt Workflows

Mature processes within mature SaaS businesses are accompanied by committed customers and years-old complex technical foundations. The big challenge lies in the implementation of AI with no disturbance of core functionality. Legacy systems can be made AI‑ready with appropriate planning for the systems’ compatibility. AI automation should be integrated with the current processes without introducing inefficiencies or usability issues that could frustrate old-time customers.

2. Scaling AI Features for Global SaaS Growth

Global market cloud-based SaaS solutions demand AI capability that supports different customers’ needs, multiple languages, and geolocation-specific compliance regulations. Massive AI deployment demands flexible and robust architecture that should be efficient at processing immense amounts of data with scalability. Predictable performance over geographies, infrastructure restrictions, and latency requirements are just a few of the challenges that AI at scale presents.

3. Dissecting Internal Resistance to AI Adoption

Not just a technical challenge, AI implementation within mature SaaS businesses must be sold to customers, execs, product managers, and developers. Resistance might be due to fears of AI stealing human jobs, and interfering with well-established processes, or questions about the real benefit AI can offer. With no clear communication about the upside of AI and no well-thought-out change management effort, the rate of adoption might be slowed, and the impact of AI-enhanced improvement within the business might be curtailed.

Challenges for Companies with In-House Software Integrating AI Agents

In-house software-dependent businesses are facing unique challenges while attempting to integrate AI agents. Unlike the case of the SaaS vendors, they are confronted with legacy systems, limited AI knowledge, and the unquantifiable nature of AI worth. The most prominent challenges they are facing are enumerated below:

1. AI with Custom, Proprietary Systems

Unlike cloud-based solutions for SaaS, enterprise solutions are built upon highly customized infrastructures that are probably not optimized for AI deployment. Compatibility issues are due to legacy infrastructures that are outdated, inflexible legacy codebases, and non-standard APIs. Further, proprietary systems are most probably going to require extensive customization for AI automation support, processing of data in real-time, or making informed decisions, increasing the implementation difficulty.

2. Internal Teams’ Lack of AI Expertise

Internal development teams are good at traditional development but are rarely AI specialists. AI model development, model tuning, and model deployment require machine learning, data science, and MLOps skills that the internal IT teams are unlikely to have. Businesses are likely to be at the mercy of AI model performance and AI reliability for years with no AI engineer at the controls.

3. Measuring the ROI and Business Value of AI Agents

Unlike SaaS models that generate revenue from AI features, companies that employ proprietary software rarely have clear ROI metrics for the implementation of AI. AI enhancements within proprietary solutions may enhance levels of efficiency, reduce operational cost levels, or enhance levels of user satisfaction but are unquantifiable using financial metrics. The inability to offer clear ROI metrics presents the challenge of acquiring buy-in from the stakeholders and justifying the necessity of more investments in AI.

Industry-Specific Agentic AI Use Cases

MobiDev AI engineers and business analysts have implemented AI agents development for software products serving different industries, including:

• Retail & Hospitality: Automated customer service, inventory management, dynamic pricing. For further details on retail chatbot development, please check out our guide.
• Fitness & Sports: Real-time performance monitoring, personal coaching.
• Health & Wellness: Telehealth chatbots, process automation, advanced data analytics.
• Manufacturing & Supply Chains: AI agents optimize production workflows, predict equipment failures, and enhance supply chain logistics through real-time data analysis.

We tailor each project to the client’s industry, ensuring precise relevance to business needs.

Building Your AI Agent with MobiDev

Incorporating agentic AI into software can be daunting, but an experienced AI consulting & development company makes it much simpler. AI agent development at MobiDev starts with a review of your business model, goals, and technical landscape. Our software architects recommend the best combination of libraries, APIs, and frameworks—whether you’re asking how to create an AI agent or require advanced integrations with LLMs. The approach accelerates development without sacrificing flexibility.

For AI product development, we adopt The CRoss Industry Standard Process for Data Mining (CRISP-DM) framework. Our AI project methodology typically involves:

• Discovery & Feasibility Assessment to determine clear business cases
• Prototyping & MVP Builds for early testing of AI capabilities
• Full-Scale Integration where the finished AI agent is installed and maintained

Our team covers all stages of the AI lifecycle: data collection, modeling, UI/UX development, integration, and ongoing optimization. If you want to save on overhead, our experts can guide you on how to build AI agents on cost-effective platforms like Microsoft Autogen or open-source platforms like LangChain. Our AI consulting approach even extends to specialized use cases across retail, hospitality, health & wellness, finance, and manufacturing, ensuring compliance and real-time performance.

Whether you need to create an AI agent for predictive analytics, anomaly detection, or automated workflows, we’ll craft a solution that blends innovation and pragmatism.

By working with MobiDev, you gain access to cross-functional experience—across ML engineering, backend/frontend development, and solution architecture—seasoned by numerous software product successes. Our team has solved an extensive range of AI-specific problems, from data integrity management to inference speed optimization at scale. This expertise directly equates to faster, more successful AI deployments.

Contact us to discuss your product needs for AI agent development.