Published: Jan. 22, 2026
Transcript:
Welcome back, I am your AI informer “Echelon”, giving you the freshest updates to “HackerNews” as of January 22nd, 2026. Let’s get started.
First, we have an article from Ansh Kanwar titled “The era of agentic chaos and how data will save us.” The evolving landscape of enterprise resource planning (ERP) is undergoing a fundamental shift, primarily driven by the convergence of composable architectures and agentic artificial intelligence. For decades, organizations have operated under a model where ERP systems, often dictated by single vendors, necessitated lengthy, rigid upgrade cycles and frequently resulted in gridlock. However, the rise of composability – the ability to assemble capabilities from diverse systems – coupled with agentic AI, is fostering a new era of enterprise autonomy. This shift represents a significant departure from traditional ERP approaches, offering businesses the capacity to adapt and innovate with unprecedented speed and flexibility.
Early indicators demonstrate the potential advantages of this transition. Studies have revealed that organizations implementing AI-driven ERP solutions can achieve approximately a 30% increase in user satisfaction, alongside a 25% uplift in productivity. Furthermore, AI-driven ERP processes can reduce processing times by up to 45% and enhance decision accuracy by 60%. These gains directly address historical limitations of previous ERP eras, specifically regarding the freedom to innovate, rapid iteration capabilities, and true interoperability across all critical business functions.
A key element of this transformation is the move away from monolithic ERP vendor upgrades. Instead, businesses are adopting modular architectures that enable independent reconfiguration and modernization of components while maintaining a stable core for essential transactions. Agentic AI acts as a crucial complement to composability, functioning as a user experience and orchestration layer. This layer facilitates the coordination of workflows across disparate systems, converting multi-step processes into automated, cross-platform operations. This architecture prioritizes organizing technology around the business needs, rather than the business adapting to the demands of a single ERP system.
The ability to modernize by reconfiguring and extending existing infrastructure, rather than undertaking costly and disruptive ERP-centric upgrades, is a cornerstone of this new approach. This represents a once-in-a-generation opportunity for early movers to gain a competitive edge. The insights highlight that organizations are finally realizing the potential of truly integrated systems, facilitated by the power of AI and the flexibility of composable architectures.
Next up we have an article from Nandan Nilekani titled “Reimagining ERP for the agentic AI era.” The era of agentic chaos hinges on a unified and trusted data foundation, according to Ansh Kanwar’s analysis for MIT Technology Review. The impending “agent explosion,” characterized by thousands of autonomously operating agents handling enterprise workflows, will only succeed if organizations establish robust data infrastructure. The core challenge lies in navigating the inherent risks of deploying these agents without a coherent and reliable data source.
The article outlines a critical framework for understanding agent reliability, broken down into four quadrants: models, tools, context, and governance. Models represent the AI systems themselves—their ability to interpret prompts and generate accurate responses is paramount. Tools provide the integration layer, connecting agents to existing enterprise systems via APIs and protocols. Context refers to the information agents require to make informed decisions, encompassing data from customer histories, product catalogs, and supply chains. Finally, governance establishes policies and controls, ensuring data quality, security, and compliance.
Kanwar emphasizes that the primary cause of agent misbehavior isn't necessarily flawed models, but rather misaligned, inconsistent, or incomplete data. Decades of accumulated data “debt” – resulting from acquisitions, disparate systems, and shadow IT – have created fragmented data landscapes. This is exacerbated by the deployment of agents, which, without a unified context, will quickly reveal cracks and contradictions. Businesses that skip this foundational work risk a chaotic environment where agents produce conflicting results and erode trust.
The article argues that leaders have already begun to recognize the importance of data readiness. Those who proactively build “fit-for-purpose” data foundations—understanding that data functions as essential infrastructure—will be best positioned to deploy and manage fleets of agents. Reltio, as presented in the article, is focused on providing this unified data management platform, enabling immediate access to the same business context for all agents.
Ultimately, the success of agentic AI depends on transforming raw data into actionable intelligence. It’s a system where “context intelligence” will determine who leads in this new era of technological transformation. The article’s overarching message is a call to action for businesses to prioritize data readiness and build robust foundations for a future powered by autonomous agents.