Top 5 must-haves to successfully build and deploy AI agents

For the past year, we've seen an explosion of talk about AI agents in the enterprise. While impressive demos and quick prototypes are everywhere, successfully implementing AI agents that can handle mission-critical business processes is entirely different. Without the proper foundation, many AI agent initiatives fall short of expectations.

Our recent survey of over 1,000 enterprise leaders, the State of AI Agent Development Strategies in the Enterprise, confirms this reality:

• 79% anticipate data challenges to impact AI agent rollouts

• 57% cite security concerns as the top barrier to success

• 38% say integration complexity is the top challenge in developing and deploying AI agents

Despite these challenges, 41% of enterprises predict that up to half of their business processes will run on AI agents in 2025. However, those organizations relying on legacy iPaaS platforms for their agent initiatives will undoubtedly lag behind, becoming trapped by one of many pitfalls like the ones mentioned above. 

For enterprises looking to deliver high-value, high-impact AI agents, the challenge is clear: you need a single platform that does more than power prototypes—it must provide production-ready agents quickly, flexibly, and safely. 

So if you are looking to...

• Deliver real business value with your AI investments

• Deploy agents that are more than just a better chatbot 

• Keep security, compliance, and IT governance at the forefront of your AI initiatives

...then this blog post is for you.

Let's review the five essential capabilities that enterprises need from their agent platforms and explain why these requirements aren't just nice-to-haves—they're absolute necessities for successful enterprise AI adoption.

• Real-time logging and 100% traceability 

• Integration  

• Access to AI-ready data 

• AI beyond just chatbots 

• Simple deployment with enterprise control

1. Real-time logging and 100% traceability

The current state: 

Many organizations today are “testing the waters” with AI agents by first deploying them in low-stakes processes, such as handling FAQs or automating routine tasks, to safely test their capabilities. However, enterprises looking to take the next step and maximize the value of AI agents must expand their use into more impactful areas such as IT incident management, financial operations, and customer retention. Even minor errors can disrupt essential business operations in these environments, making transparency and traceability non-negotiable. 

When AI agents handle these crucial business processes, "black box" products that offer little to no visibility into the decisions agents make won't cut it. This lack of transparency complicates debugging and raises compliance and trust issues. You need a transparent process that lets you know exactly what's happening at every step.

The next step:

Enterprises need platforms that provide detailed logs of every agent action, ensuring complete visibility and accountability. Modern AI integration platforms with a step-by-step execution architecture make this possible by providing the following:

• Complete visibility into each decision and action

• Audit trails that track every data point used

• Real-time monitoring of agent executions

This level of transparency isn't just about debugging – it's about building trust in your AI systems and ensuring compliance with regulatory requirements.

2. Integration 

The current state:

A truth often overlooked is that building effective AI agents is fundamentally an integration challenge. For enterprises, siloed systems and data remain their most significant barriers to effective AI adoption, with 38% of organizations citing integration complexity as an ongoing challenge in developing and deploying AI agents. Disconnected systems and data lead to a fragmented experience and restrict the agents’ potential impact. 

The next step:

Your agents must seamlessly connect with every system in your technology stack—CRM, ERP, data lakes, and even legacy systems—to deliver meaningful results.

The AI agents that do this best are those built on an AI-ready iPaaS foundation and can provide:

• Native connectors to hundreds of enterprise systems

• Real-time data synchronization capabilities

• Secure API management

• Custom connector development tools (CDK)

This means your agents can work with any platform, whether it's your legacy CRM, modern cloud services, or custom internal tools.

3. Access to AI-Ready data

The current state:

Many enterprises struggle with data challenges—79% anticipate issues such as managing unstructured data and fragmented pipelines impacting their AI agent rollouts. 

AI agents are only as impactful as the data they have access to. More importantly, they need access to your real-time business data to make relevant decisions. In other words, good data equals good outcomes. Bad data equals…well, you get the idea.

The next step: 

An effective platform should provide real-time access to clean, organized, and AI-ready data. The best platforms do this by offering:

• Native vector tables for efficient semantic search

• Pre-built indexing templates for common data sources

• Complete control over data processing pipelines

• Real-time data updating capabilities

You maintain complete control over how your data is processed, indexed, and used, ensuring your agents always use the most current and relevant information.

4. AI beyond just chatbots 

The current state:

While conversational AI agents like chatbots are valuable, enterprises need agents capable of taking real action. Think about resolving IT tickets or even automating supply chain processes. 61% of enterprises identify IT service desk automation as a top priority for AI agents, further proving that organizations are looking beyond chatbots at agents that support proactive and operational capabilities across departments.

The next step:

You need to be able to deploy in chat interfaces for quick wins and to meet the users where they are today. Modern businesses need agents that can work autonomously in the background, responding to events and triggering actions across systems and departments. 

Look for platforms that support:

• Event-based triggers for automated agent activation

• Asynchronous processing capabilities

• Multi-step workflow orchestration

• Complex decision-making patterns

This means your agents can automatically process invoices, monitor system health, optimize supply chains, and much more – all without requiring direct human interaction.

5. Simple deployment with enterprise control

The current state:

Enterprises face unprecedented pressure to develop and deploy AI agents quickly to stay competitive, but speed often comes at the expense of governance and control. While 58% of enterprises believe agents should move from prototype to production in 1-3 weeks, only 30% achieve this timeline today.

The next step:

The right platform lets teams rapidly deploy agents without sacrificing security or governance. By combining a low code UI for fast prototyping with native controls for compliance, enterprises can build and innovate confidently while meeting operational demands.

The best no-compromise low-code builders deliver:

• Rapid deployment capabilities

• Built-in governance controls

• Easy model switching and updates

• Team-based development workflows

This approach allows business teams to move quickly while ensuring IT maintains appropriate control and oversight.

Getting started

The race to implement enterprise AI agents has truly started. Still, success requires more than picking the latest chatbot platform; You need a foundation to support the complex requirements of enterprise-scale AI deployment.

Focusing on these five essential capabilities— Real-time logging and 100% traceability, integration, access to AI-ready data and AI beyond just chatbots —can help you build agents that deliver real business value while meeting enterprise security, compliance, and governance requirements.

Tray.ai’s Merlin Agent Builder allows you to deliver high-value AI agents by: 

• Accelerating delivery timelines, moving agents from prototype to production in weeks

• Providing seamless integration across systems so agents are constantly working with fresh, real-time data

• Delivering governance and scalability so teams can build without compromise

Ready to start building enterprise AI agents? Let's discuss how we can help you implement agents beyond chat to deliver fundamental business transformation.