Inside Tredence’s Milky Way, Where AI Agents Learn to Work Like a Team

Data analytics and AI services company Tredence is taking a bold step in enterprise AI with the launch of Milky Way, its new multi-agent decision intelligence platform. Positioned as a leap beyond traditional AI tools, Milky Way has been designed to function less like software and more like a team of collaborators that can think, reason, and solve problems alongside human decision-makers.

Praveen Koushik Satyanarayana, director of customer experience management at Tredence, has been closely involved in conceptualising and building the product. Talking about the inspiration behind Milky Way, he told AIM: “Complex functions within the enterprise, like the job of an analyst, require numerous steps. There is no one tool today that does all of those things.”

Milky Way introduces more than 15 prebuilt agents for business roles and over 50 specialised agents trained on real-world enterprise scenarios. These agents act as digital co-workers across functions, including marketing, supply chain, customer analytics, shopper insights and product lifecycle management.

From Tools to Teammates

Tredence is framing Milky Way as a transition from conventional AI-driven tools to what Satyanaryana describes as teammates. Instead of simply generating an output based on a prompt, Milky Way deploys multiple specialised agents that interact with each other to arrive at meaningful insights.

This approach is particularly relevant for exploratory analytics, where the end goal is not always known in advance and the system must work through ambiguity, much like a human analyst would. 

Satyanarayana explained the difference with an example. “When you know the output you want, you can prompt your way to it with AI. But generating insights is different,” he said, adding that this is where autonomous systems come into the picture, which can think and reason with you, rather than just produce a response.

According to Tredence, early deployments in retail, consumer packaged goods, telecommunications and healthcare have shown a five-fold improvement in time-to-insight and 50%  lower analytics costs. A global retailer reportedly cut manual effort in merchandising operations by 60% through assortment planning and pricing optimisation. Healthcare organisations used the system to automate patient data aggregation and triage.

Inside the Milky Way

The architecture of  Milky Way reflects this philosophy. It is built around a network of domain-specific and specialised agents, orchestrated on a single platform. These agents break down complex questions into smaller, manageable steps. 

For example, when asked a diagnostic question like “why are my sales down?,” Milky Way deploys a series of agents that work together in sequence. A clarification agent refines the question, followed by a hypothesis agent that proposes possible reasons for the decline. A data sufficiency agent then connects the business terms with enterprise data, while a text-to-SQL agent retrieves the relevant information. Finally, analysis agents process the data, generate insights, and compile reports. 

Satyanarayana said that this kind of orchestration allows Milky Way to behave more like a team of analysts than a single tool.

Under the Hood 

Another distinguishing feature of  Milky Way is its composable architecture. Rather than binding clients to a single vendor or technology stack, the platform has been designed to integrate with both open-source and proprietary tools. Enterprises can swap out specific components, such as text-to-SQL agents, with their own solutions if they already exist.

“We’re not married to one model or vendor,” Satyanarayana said. “Our goal is to solve the problem. If a client already has a capability within their ecosystem, we can integrate that.”

This flexibility is expected to be particularly appealing to large enterprises that already have significant investments in platforms such as Snowflake, Databricks, or Azure. Milky Way sits on top of existing data systems without requiring companies to replicate or migrate data, which reduces friction and accelerates adoption.

The Bigger Vision

Beyond the product’s immediate capabilities, Tredence sees Milky Way as part of a broader shift in how AI will augment enterprise decision-making. The company is developing custom benchmarks to evaluate the system’s ability to replicate the reasoning processes of expert analysts and is also experimenting with proactive background agents that can surface insights without explicit prompts.

Satyanarayana summed up the vision by explaining that the goal is to make insights available at scale, 24/7. In this model, a wealth advisor could quickly get answers about a client’s portfolio, a store manager could find out why stocks are running low, and a marketing manager could see why customer churn is rising. The idea is that insights should no longer depend on technical expertise or long wait times, they should be instantly accessible, anytime.

If Tredence can deliver on its vision, Milky Way could mark a turning point in how organisations think about decision intelligence.

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