AI agents are all the rage these days. Poised as the next big thing after Gen AI…is there substance underneath all the hype? The answer is a resounding yes.

For instance, the 2024 State of AI Agents report revealed that 51% of AI professionals are already using AI agents, while 78% of enterprises and mid-sized companies have active plans to put AI agents into production. However, doing this successfully requires paying attention to certain key factors.

But before we get to those factors, let’s quickly review what AI agents are.

What Are AI Agents?

An AI agent refers to any software program or system that can perform tasks without human intervention. These agents can reason, plan, memorize, make decisions, learn, and adapt with a certain degree of autonomy.

Learn More: What Are AI Agents? Definition, Types, Applications for Enterprises, and More

7 Key Considerations for Enterprises Building AI Agents

Much of the enthusiasm around building AI agents is to automate routine tasks such as scheduling, data entry, and content creation. However, there’s huge potential for more complex use cases across multiple industries, such as healthcare (diagnosis, patient data processing), finance (fraud detection, portfolio optimization), and supply chain (invoice processing, accounts reconciliation).

In fact, McKinsey predicts that AI agents can automate up to 70% of work hours in the global economy.

Nevertheless, developing and deploying more advanced agents requires several key factors to be aligned. For instance, data needs to be in the right place and in the correct format for AI agents to be of value.

In fact, 20% of organizations believe that data is crucial for capturing value from their GenAI initiatives.

That said, let’s look at the seven most crucial considerations for enterprises looking to build AI agents:

1.   Data Quality

AI agents are only as good as the data they’re trained on. What this means is that missing, inconsistent, or outdated data fields lead to unreliable outputs.

Organizations that focus on maintaining AI data quality can ensure their agents work effectively. This can be done by implementing automated data validation to clean, standardize, and validate data before any errors can impact agentic AI performance.

2.   Data Integration & Access

AI agents need timely access to well-integrated data to function effectively. However, enterprise data is spread across multiple systems, formats, and sources, ranging from databases and cloud applications to unstructured documents and APIs.

Organizations should ensure their AI agents have seamless access to integrated data sources during development and deployment. Here’s how:

• Establish a Unified Data Layer: Creating a central data integration framework allows AI agents to pull from multiple sources without dealing with fragmented data silos.
• Standardize Data Formats: Implementing common data standards and transformation processes ensures AI agents can interpret and use information consistently, regardless of its source.
• Enable Secure API Connectivity: Using APIs to bridge AI agents with existing applications ensures real-time access to structured and unstructured data while maintaining security protocols.
• Implement Role-Based Access Controls: Ensuring the right teams have appropriate access while maintaining security and compliance safeguards is crucial for enterprise-wide AI adoption.

Build AI Agents in Hours Instead of Weeks

Astera takes the grunt work out of building AI. Our visual builder lets you design, develop, and deploy AI agents with simple drag-and-drop, a vast library of functions, and a variety of pre-built templates.

Connect With Us to Learn More.

3.   Technical Expertise

Building AI agents requires extensive, specialized knowledge of machine learning (ML), natural language processing (NLP), and AI training. This is perhaps the biggest roadblock for enterprises looking to deploy AI agents; lack of AI expertise or coding resources to develop, test, and refine agents.

Enterprises can turn this challenge into an opportunity by simplifying their agentic AI development and making it more accessible to non-technical users. Here’s how:

• Visual AI Agent Builders: Building AI agents using a drag-and-drop solution engages business and data teams to develop and manage AI agents without needing to write extensive code. Using a solution with a visual interface not only makes the entire process easier but also allows domain experts to develop AI agents for their specific use cases without having to rely on technical resources.
• Pre-Built Templates and Workflows: Another way to overcome a lack of technical expertise is to rely on agentic AI solutions offering templates, reusable components, and ready-made AI workflows that can easily be fine-tuned and tweaked as per the organization’s specific needs.

Pre-built templates for common tasks such as customer support, document processing, and data extraction can help organizations create agents without starting from scratch.

4.   Scalability

AI agents that perform well in small-scale tests may not perform as expected when deployed across an enterprise. This happens due to the inevitable increases in data volume and workflow complexity, which makes maintaining efficiency, reliability, and performance a challenge when scaling agentic AI.

Ensuring scalability in your agentic AI initiatives requires flexible deployment, optimized resource management, and seamless data integration. Here’s how:

• Adopt a Distributed Architecture: Using cloud-based or hybrid environments allows AI agents to handle increasing workloads without overloading infrastructure. Distributed processing ensures smooth operation as demand grows.
• Enable Modular and Reusable AI Components: Designing AI agents with modular components makes it easier to scale by adding new capabilities or expanding to different use cases without rebuilding from scratch.
• Implement Robust Monitoring: Real-time analytics, performance tracking, and automated updates allow businesses to maintain autonomous AI agents at scale without constant manual intervention.

5.   Security and Compliance

Security and compliance are top considerations for AI agents, especially when dealing with sensitive enterprise data. In order to avoid data breaches, unauthorized data access, and non-compliance with regulations such as HIPAA, GDPR, or SOC 2, organizations need to implement proper safeguards.

A strong security and compliance strategy when building AI agents involves proactive risk management, robust access controls, and adherence to regulatory standards. Key measures include:

• Implement Role-Based Access Controls (RBAC): Restrict AI agent access based on user roles to ensure only authorized personnel can view or modify sensitive data.
• Keep Data Within Secure Environments: Instead of sending data to external servers, AI agents should operate within the enterprise’s infrastructure, whether on-premises or in a secure cloud environment.
• Encrypt Data in Transit and at Rest: Robust encryption protocols protect sensitive data from unauthorized access, ensuring compliance with regulatory requirements.
• Maintain Audit Trails and Logging: Comprehensive logging of AI agent activity helps organizations track data usage, detect anomalies, and ensure compliance with industry standards.

Apart from these measures, companies should also ensure their AI agents are designed to follow data governance best practices. Similarly, AI agents in production should be put under continuous monitoring, vulnerability assessments, and compliance audits.

If You Know Your Data, You Can Build Your AI

Astera empowers domain experts in practically every field to build AI agents in hours instead of weeks. Just drag-and-drop or start with our templates to design, develop, and deploy agents effortlessly.

Learn More

6.   Optimization and Refinement

AI agents are not static solutions—they need ongoing refinement to maintain accuracy, efficiency, and relevance. Without continuous monitoring, businesses risk deploying agents that degrade over time, fail to adapt to new data patterns or produce unreliable outputs.

A well-defined monitoring and optimization process ensures AI agents deliver consistent value. Key strategies include:

• Implement Real-Time Monitoring: Track AI agent interactions, detect anomalies, and flag performance issues before they impact operations.
• Easy A/B Testing for Iterative Improvements: Continuously refine AI prompts, workflows, and responses by comparing different AI agent versions and selecting the best-performing model.
• Retrain AI Models with Updated Data: Periodically update models with new enterprise data to prevent performance degradation and improve adaptability.
• Automate Testing and Validation: Implement automated validation frameworks to check AI agent outputs for consistency and compliance before deployment.

7.   Customization and Flexibility

When it comes to AI agents, their effectiveness and ROI lie in how well they align with your specific business processes, data structures, and operational requirements. This means customization and flexibility are key considerations for enterprises looking to leverage agentic AI.

Enterprises looking to build adaptable AI agents should strike a balance between ease of use and customization capabilities. Here’s how that can be done:

• Modular Design: AI agents should be built with configurable components that allow businesses to tailor functionality without altering core logic.
• Customizable Workflows: Organizations should be able to define AI-driven processes that align with their unique needs, whether for customer interactions, automation, or data analysis.
• Support for Multiple AI Models: Businesses should have the option to use different AI models—including commercial LLMs, open-source alternatives, or proprietary models—based on performance, security, and cost considerations.
• Extensible via APIs: Providing APIs allows businesses to integrate AI agents into custom applications and workflows with minimal development effort.

Summing it Up

Building AI agents that deliver real value requires considering the key factors we’ve discussed in this post. Despite the game-changing impact of AI agents, failing to prioritize these considerations can hold organizations back.

By taking a strategic approach and prioritizing these factors in their roadmap, enterprises can build smarter, more accurate and more reliable AI agents and agentic workflows.

Plus, by leveraging agile, visual development, modular architectures, and advanced NLP techniques, organizations can build AI agents that are efficient, adaptable, and scalable.

With the right foundation in place, AI agents can go beyond routine automation and deliver real ROI across hundreds of use cases in practically every industry.

Enterprise-Ready AI Agents With Astera AI Agent Builder

With Astera, enterprises can build AI agents at scale using a visual interface. With the intuitive, drag-and-drop UI, you can empower domain specialists to experiment and build AI agents.

Build, refine, and deploy AI agents in hours—trained on your data.

What’s more, with Astera’s award-winning data integration capabilities, you can integrate your AI agents with your enterprise data stack for effortless model training and information retrieval.

The result? AI agents that are made exclusively for you, by you.

Learn more about Astera AI Agent Builder or connect with us to discuss your agentic AI needs.