#ai interoperability

As enterprise AI deployments mature beyond experimental pilots, intelligent agent architecture has emerged as a critical discipline bridging AI model development and production-grade business systems. Unlike monolithic AI applications, agent-based modeling enables autonomous decision-making entities that perceive their environment, process multi-modal data, and execute actions aligned with business objectives. Organizations from Microsoft Azure AI to Google Cloud's Vertex AI have standardized on agent frameworks that support distributed reasoning, dynamic task allocation, and real-time inferencing at scale.

The shift toward Intelligent Agent Architecture reflects a fundamental change in how enterprises approach AI solution lifecycle management. Rather than deploying isolated machine learning pipelines, modern implementations orchestrate multiple specialized agents—each optimized for perception, planning, or execution—within a unified cognitive computing resource allocation framework. This architectural pattern addresses integration complexity across legacy systems while enabling instance-based learning and personalization that adapts to individual user contexts and organizational workflows.

Core Components of Enterprise Agent Systems

Production-ready intelligent agents comprise several interdependent layers. The perception layer handles automated entity recognition and analysis, processing structured data from CRM systems alongside unstructured inputs like customer communications. Natural language processing optimization transforms raw text into semantic representations that agents use for reasoning. The decision layer employs predictive modeling efficiency techniques to evaluate options against defined business rules and learned patterns. Finally, the action layer interfaces with enterprise intelligence systems to execute decisions—whether updating records in Salesforce, triggering workflows in Oracle applications, or routing requests through chatbot orchestration platforms.

Scalability challenges demand careful architectural choices. IBM's Watson deployment patterns demonstrate how intelligent data flow orchestration distributes workloads across compute resources, preventing bottlenecks during peak demand. Deep neural networks powering agent reasoning require substantial memory and processing power, making cognitive load balancing essential for cost-effective operations. Organizations implementing enterprise AI solutions must architect for horizontal scaling, allowing additional agent instances to join the system as transaction volumes grow without degrading response times or accuracy.

Integration Patterns and Interoperability

The practical value of intelligent agents depends on seamless integration with existing digital transformation architecture. AI interoperability testing validates that agents correctly consume APIs, respect data governance policies, and handle exceptions gracefully when upstream services fail. Multi-modal data processing and synthesis capabilities enable agents to correlate information from disparate sources—combining customer purchase history, support ticket sentiment, and real-time behavioral signals to inform next-best-action recommendations.

Adaptive learning system implementation ensures agents improve through experience. Unlike static rule engines, modern agent architectures employ continuous learning loops where agent decisions and outcomes feed back into machine learning operations pipelines. This creates a flywheel effect: better decisions generate richer training data, which produces more accurate models, enabling even better future decisions. However, this requires robust robustness evaluation frameworks to detect when agents drift toward undesirable behaviors or exploit shortcuts that satisfy narrow performance metrics while undermining broader business goals.

Conclusion

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Ralph Gootee, CTO and Co-Founder at TigerEye – Interview Series

Ralph Gootee, CTO and Co-Founder at TigerEye, leads the development of a business simulation platform designed to enhance strategic decision-making, planning, and execution. By leveraging advanced time-aware AI technology, TigerEye enables organizations to streamline planning processes, simulate various scenarios, and make data-driven decisions more efficiently.

Founded by Gootee and former PlanGrid executives, TigerEye addresses common challenges in business planning, such as outdated spreadsheets and prolonged planning cycles, with a focus on adaptability and predictable growth. The platform integrates principles from industries like construction and software QA to provide dynamic solutions that help businesses optimize operations and scale effectively.

What inspired you to start TigerEye, and how did your previous experiences with PlanGrid influence your vision for the company?

I’ve always found data to be a challenge. Back when we built my last company, PlanGrid, tools like Looker and Redshift were just coming out. The concept of insights was new. Mixpanel and Amplitude were still in their early days. These products were so fresh that you had to build your own data engineering team to handle any kind of data insights.

At PlanGrid, we assembled an incredible team with PhDs and talented leaders who did impressive work: identifying hot leads, analyzing customer connections, and calculating ARR. But it took a 10-person team, was expensive, and left analysts feeling like ticket crunchers, running SQL queries to answer segmentation and growth questions. When they eventually moved on to lead data science teams elsewhere, the remaining team was often left struggling to make sense of the dashboards they left behind, leading to significant wasted time. Additionally, our CFO manually verified those numbers to ensure accuracy.

As a board member at other companies, I saw the same pattern: disconnected dashboards that were hard to piece together into actionable insights. During the Autodesk acquisition of PlanGrid, these challenges became even clearer. Managing two Salesforce environments and coordinating basic back-office tasks like CRM, ERP, and marketing was a struggle. Even determining which campaigns were working was a mystery. These frustrations inspired the vision for TigerEye: a way to make data seamless, actionable, quick and accessible.

TigerEye offers a flexible AI solution for go-to-market teams. What challenges in the market did you identify that led you to design a conversational AI for business intelligence?

Go-to-market analytics often feel overwhelming as it is packed with numbers, stats, and heavy math. The process of asking creative, investigative questions is clunky. You might create a ticket for the data team, asking for something like a win rate graph. There’s back-and-forth clarification, delays, and sometimes you realize you asked the wrong question. For most people,  it’s neither an enjoyable nor a fast process especially for those without the authority of a C-Suite executive to fast-track responses.

Conversational AI changes that. Imagine just saying, “Show me win rates for the West Coast in pink versus the East Coast in brown, over the past four quarters, in a bar chart.” A conversation like that takes seconds and so does the output. We designed TigerEye to give users an intuitive “junior analyst” they can talk to — always available to create insights without the need for a clunky interface.

What were the most significant hurdles you faced during the early stages of TigerEye’s development, and how did you overcome them?

One major surprise was the sheer scale of data we encountered, regardless of company size. Even mid-market companies often have vast amounts of data that change frequently. Existing tools like Looker couldn’t handle these workloads efficiently; we saw load times of 10–12 seconds for a single graph. That’s unacceptable for today’s fast-paced business environment.

To address this, we had to innovate. We integrated DuckDB for faster query execution and chose Flutter for building a lightweight, efficient interface. Additionally, we contributed back to the open-source community by developing and maintaining DuckDB.Dart, enabling seamless integration with Dart and Flutter environments. These technologies allowed us to optimize for speed, flexibility and scalability.

As a co-founder, how did you and your team prioritize features and capabilities for TigerEye’s launch?

We started by putting the entire company’s resources behind the AI Analyst vision. This meant every front-end and back-end engineer contributed. The nature of an AI analyst required a full-company effort because it’s not just about text output; it’s about providing interactive widgets, configuring simulators, and enabling analysts to take meaningful action. For example, one feature lets users configure a future plan to add 10 reps to the West Coast seamlessly, which involves designing a highly interactive and intuitive system.

The development process had its ups and downs, but the technical backbone was built on rigorous evaluation. This became the core of our prioritization. Evaluation is where the real work happens. We’re constantly asking, “Did this change make the system better or worse?” We started with our engineering team and our domain experts and eventually evolved to capturing customer questions to refine our system further.

We introduced an automated test suite where the AI evaluates itself and assigns a score to determine if changes are improvements. To ensure accuracy, we still conduct human evaluations weekly to prevent biases like an LLM giving itself top marks. This dual-layer approach has been crucial to getting TigerEye to a “1.0” state and continually raising the bar.

Finally, achieving domain-specific alignment was a major focus. Sales and go-to-market operations demand precise, specialized answers, and alignment across stakeholders isn’t always straightforward. This is why domain expertise and real-world customer feedback were critical in shaping TigerEye into the platform it is today.

How does TigerEye’s approach differ from traditional BI tools, and what impact has this had on adoption rates among businesses?

TigerEye was built from the ground up with AI and mobile, offering a solution that is inherently portable and designed to answer questions quickly. Unlike traditional BI tools, which are slow and often require extensive configuration, TigerEye prioritizes speed and ease of use through conversational AI.

Our graphs and widgets are highly flexible, with interactive visuals that allow users to explore data intuitively. The AI doesn’t rely on generic, surface-level information that can lead to inaccurate responses; instead, it’s specialized to deliver precise, structured metrics tailored to each business.

Whether for startups, midmarket, or enterprise companies, TigerEye ensures consistency by grounding all calculations in SQL, enabling both front-end and AI-driven queries to deliver the same reliable numbers. We also provide transparency by showing customers the math behind our analysis, ensuring they understand exactly how the TigerEye platform arrived at its responses. This commitment to clarity helps build trust and confidence in the insights delivered.

The result is an AI platform that delivers strong customizability while empowering teams to access actionable insights independently, allowing data teams to focus on more strategic tasks. This approach has accelerated adoption among businesses looking for intuitive, scalable, and precise tools to enhance their decision-making.

How does TigerEye leverage AI to adapt and learn from CRM, ERP, and marketing automation changes in real time?

TigerEye uses AI, including Retrieval-Augmented Generation (RAG) and integrations with real-time APIs, to adapt dynamically to changes in CRM, ERP, and marketing automation platforms. We also combine GenAI with more traditional machine learning and simulation theory to give our AI the ability to predict the future. By connecting directly to these systems, our company continuously monitors updates, such as new customer records, changes in deal stages, or campaign performance metrics, ensuring insights remain current and actionable.

Our AI Analyst doesn’t just passively report data; it learns and evolves with customer workflows. For example, if a sales team modifies its pipeline structure, TigerEye quickly identifies the changes and adjusts its calculations, forecasts, and recommendations accordingly. This real-time adaptability eliminates manual updates and ensures leadership and teams always have an accurate, up-to-date view of their go-to-market performance.

Also, TigerEye’s flexibility allows it to work across multiple systems, ensuring seamless integration and alignment. Whether it’s Salesforce, HubSpot, NetSuite, or other platforms, TigerEye’s AI enables teams to cut through complexity, delivering timely, reliable insights that drive smarter, faster decision-making.

With increasing complexity in go-to-market operations, how does TigerEye simplify decision-making for leadership and teams?

Actionable insights through conversational AI. Traditional BI tools often require teams to navigate cumbersome dashboards, wait for data teams to generate reports, or manually piece together metrics across siloed systems. TigerEye eliminates these bottlenecks by providing instant, AI-driven answers tailored to leadership and teams’ needs.

Our AI Analyst functions like a proactive, junior team member, capable of responding to questions such as, “What’s my win rate in Q4 across regions?” or “How would adding five reps to the East Coast impact ARR?” The platform delivers insights in seconds without the need for data modeling or extensive setup.

By integrating AI with tailored business intelligence, TigerEye ensures that all metrics are accurate, consistent, and aligned across the organization. Leadership gains clarity on strategic decisions, while teams benefit from tools that surface trends, predict outcomes, and reduce the noise of operational complexity. TigerEye helps business leaders make faster, smarter decisions without the heavy lift.

How do you see conversational AI transforming business intelligence over the next five years?

Business intelligence is currently at a crossroads. Many tools remain stuck in an older or acquired state. They’re slow to innovate, lacking new products, and overly generalist in their approach. These legacy solutions weren’t built from the ground up to integrate with large language models or to offer AI interoperability. In most cases, they’re trying to retrofit outdated systems with unproven AI solutions, which isn’t moving the needle.

Conversational AI will drive a new breed of specialized BI applications. These tools won’t require teams to spend countless hours customizing and building solutions — they’ll be tailored from the outset to address specific needs in finance, sales, marketing, construction, oil and gas, and other industries. Each market is evolving differently, and specialization is key.

Foundational AI models like OpenAI, Anthropic, and Mistral will continue to handle broad, generic applications, but the future of BI lies in specialized vertical solutions that address unique problems. Specialized AI tools for BI will replace the current one-size-fits-all approach, enabling businesses to extract insights faster and more accurately. It can deliver precision and actionable insights within its domain. This shift will redefine BI as we know it.

After serving as a visiting partner at Y Combinator, how has mentoring startups influenced your leadership style or approach to innovation?

YC taught me the importance of prioritizing people. I learned to focus my energy on founders who were hungry, open to feedback, and relentlessly tenacious. Those traits — grit and adaptability — are hallmarks of successful teams, and I’ve carried that into TigerEye.

Another lesson was recognizing the value of diversity, both in thought and background. At YC, I saw firsthand how founders from underrepresented groups often brought incredible resilience and creativity to the table. It’s a perspective that’s shaped how we build and lead at TigerEye today. Diversity strengthens teams and drives innovation.

What’s your vision for the future of TigerEye, and how do you plan to expand its impact across industries?

TigerEye is first and foremost an AI company. Our goal is to bring the innovations we see in consumer AI, like the seamless interaction in tools like Perplexity and Cursor, into the enterprise. Imagine a personal assistant that you can ask for insights anywhere, on any device. Need to know why deals stalled in Q2 or what would be required for you to double your sales headcount in a certain region while you’re on the move? You ask, and it’s there instantly, accurate and consistent across the company.

The future of TigerEye is about simplifying access to data and making insights ubiquitous, whether you’re using a mobile app, wearing a smartwatch, or asking for a report in Slack. We’re focused on creating tools that make data-driven decision-making effortless.

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Composable AI: A Flexible Way to Build AI Systems

Artificial intelligence (AI) is everywhere these days. It is helping us shop online, diagnose diseases, and even drive cars. But as AI systems get more advanced, they also get more complicated. And when things are complicated, they are harder to change, scale, or fix. That is a big problem in a world that is constantly changing.

Composable AI offers a new approach to solving this problem. It focuses on dividing systems into small, independent modules—like pieces of a puzzle. Each module is designed to perform a specific task, such as processing data, understanding language, or recognizing images. These parts can be swapped, upgraded, or combined as needed. This approach makes AI more flexible, easier to maintain, and better suited for the real world. Let’s explore how this approach works and why it matters.

The Problem with Traditional AI Systems

Most traditional AI systems are built as a single, tightly connected unit. Everything is linked together, which can make the system very efficient for one specific task. But this design also comes with some challenges:

Hard to Change If you want to update or improve one part of the system, you often have to rework the whole thing. It is like trying to fix one piece of a car engine—you might have to take the whole engine apart.

Scaling Issues Adding new features or handling more data can be a complex task. You cannot just plug in new parts; you often have to start from scratch.

Maintenance is Tricky Fixing bugs or making updates takes a lot of time and money. Even small changes can mess up other parts of the system.

These issues make traditional systems a poor fit for industries that need to adapt quickly, like healthcare, retail, or finance.

What Is Composable AI?

Composable AI takes a different approach. Instead of one big system, it breaks things into smaller, separate modules. Each module is designed to do one specific job, like analyzing data, processing text, or recognizing images. These modules can work alone or together.

For example, imagine an online store that uses AI to recommend products. A traditional system might handle everything—data collection, user profiling, and suggestions—in one pipeline. With Composable AI, each task would be handled by a separate module. You could upgrade the recommendation engine without touching the rest of the system. More details can be found at the guide to composable AI and composable AI resources.

The Key Ideas Behind Composable AI

Composable AI is built on a few simple ideas. Here is how it works:

Modularity Break AI into small, independent parts. Each module does one thing, like cleaning data or making predictions. This keeps things simple and easy to manage.

Reusability Use the same modules for different projects. For instance, a module that analyzes customer feedback can work in a call center, on social media, or in product reviews. This saves time and money.

Interoperability Make sure modules can communicate with each other. Standard APIs and protocols help different pieces work together, even if they come from different teams or vendors.

Scalability Add new features by plugging in extra modules instead of overhauling the whole system. Need better image recognition? Just add a new module for that.

Adaptability Swap out old modules or add new ones without breaking the system. This is great for industries that change fast.

Why Composable AI Matters

This modular approach offers many benefits. Let’s break them down:

It is Faster to Build Developers do not have to start from scratch. They can use existing modules and get systems up and running quickly. For example, a retailer launching a recommendation system can plug in ready-made modules to analyze user behavior and suggest products.

It Saves Money Building AI is expensive. Reusing modules across projects reduces costs. For instance, a logistics company might use the same prediction module in multiple apps, from delivery tracking to customer notifications.

It is Flexible As needs change, businesses can swap out modules or add new ones. If a hospital gets better imaging technology, it can replace the old module without reengineering the whole diagnostic system.

Maintenance is Easier If one module breaks or gets outdated, you can replace it without affecting the rest of the system. This reduces downtime and keeps things running smoothly.

Where Composable AI Is Making an Impact

Composable AI can make a meaningful impact across various industries. Here are a few examples:

Healthcare AI systems in hospitals can use separate modules for tasks like diagnosing diseases, analyzing medical images, and predicting treatment outcomes. If a new imaging technique is developed, the system can easily integrate it.

E-Commerce Online stores can personalize shopping experiences by combining modules for tracking user behavior, analyzing preferences, and recommending products. Businesses can quickly adapt to shifting consumer trends.

Finance Banks and financial institutions can use modular AI for fraud detection. Modules can analyze transactions, monitor account activity, and flag unusual patterns. If new threats emerge, they can update specific modules without overhauling the whole system.

Autonomous Vehicles Self-driving cars rely on AI for object detection, decision-making, and more. A modular approach allows manufacturers to improve one function, like pedestrian recognition, without redesigning the entire software.

Challenges of Composable AI

While the benefits are clear, implementing composable AI is not without challenges. Here are some hurdles developers and organizations face:

Standardization For modules to work together, they need common standards. With standard interfaces and protocols, integrating components from different sources becomes easier. The industry is making progress in this area, but it is still a work in progress.

Complexity Managing multiple modules can introduce complexity. Managing their interactions, especially in real-time applications, requires careful design. For example, ensuring that data flows smoothly between modules without delays or errors is critical.

Security Each module in a composable AI system has a potential vulnerability. If one part is compromised, it can put the entire system at risk. Strong security practices, like regular updates and robust testing, are essential.

Performance Modular systems may face performance trade-offs. Communicating between components can introduce latency, especially in high-speed applications like autonomous vehicles. Optimizing these interactions is a key challenge for developers.

The Bottom Line