Your ML Models Are Tuning You Out, Context Is the New Code, and Agents Are Redefining APIs

Biweekly Data & Analytics Digest: Cliffside Chronicle

Author

Josh Miramant
13 Aug • Estimated Reading Time: 8 minutes

The Rise of Context Engineering: The Missing Link in Enterprise AI

Context engineering is the real unlock for enterprise AI, not prompting. LLMs are becoming more capable, and the bottleneck is shifting to feeding them the right information, instead of phrasing instructions. It’s a blend of retrieval pipelines, ranking, formatting, and human-in-the-loop logic. Done right, context engineering turns brittle LLM prototypes into reliable AI systems that operate with nuance and up-to-date business understanding.

Most GenAI failures context problems more than they’re model problems. Enterprises are sitting on rich internal knowledge, but lack the pipelines to route it into LLMs effectively. Technical leaders investing in RAG, copilots, or agentic systems are finding out that this is where data engineering meets AI infrastructure. More importantly, it’s changes how we think about team roles, system architecture, and long-term model performance.

Your LLMs prompts are fine, but you definitely need better inputs. If you don’t have a context engineering strategy, your GenAI roadmap isn’t ready.

When Models Stop Listening: The Silent Failure Undermining Your ML Systems

Feature collapse is a subtle but dangerous phenomenon where machine learning models begin to rely on only a narrow subset of inputs and completely ignore the rest. Data distributions will shift over time, and models will inconspicuously degrade. It’s harder to detect than full on data drift, but potentially just as destructive. It’s important to understand how feature collapse happens, why it often goes unnoticed in production, and how techniques like input dropout, data augmentation, and representation monitoring can help mitigate it.

If you’re running ML systems in production, this should keep you up at night. Teams can be hyper-focused on accuracy and completely miss that their models are becoming brittle and lazy. It’s especially risky in tabular and business-critical domains where models learn to game proxies rather than build robust representations.

This article gives technical leaders a language and framework to start addressing a root cause of long-term model failure. Because if you’re not testing for feature collapse, you might be flying blind.

Why Ops, Analytics, and Decisions Keep Missing Each Other Completely

There is a persistent, unstable relationship between Operations, Analytics, and Decision-Making. These three domains orbit each other but rarely align, which leads to delays, blame, and broken feedback loops. Why? Because Ops teams move fast but optimize for delivery, analytics teams focus on interpretation, and decision-makers need timely, trusted insights to act. Misaligned incentives, asynchronous timelines, and tooling fragmentation can prevent true data-driven decision-making, especially at scale.

This hits home for any technical leader who’s tried to connect the dots from pipeline to business outcome. Analytics delivers insights that are already stale, Ops changes metrics definitions mid-flight, and leadership is left flying blind. No dashboard will be the solution. You fix with alignment, ownership, and clear interfaces between teams.

If your data org feels like it's always "almost aligned," it’s probably caught in this gravitational pull. This article articulates the underlying dynamics you may be facing.

Agentic AI Is Redefining How APIs Are Discovered and Used

Agentic AI systems are fundamentally changing the way APIs are consumed. Traditionally, developers rely on documentation, SDKs, and manual integration, but agentic AI flips that. APIs become discovered dynamically, with agents learning capabilities through exploration, description, and trial. Tools like OpenAPI specs and API catalogs are becomeing machine-readable blueprints for AI to understand and interact with services autonomously, rather than simple developer aids. They’re moving from static consumption to interactive discovery.

For technical leaders, it raises strategic questions: How do we expose APIs in a way agents can use? How do we secure systems when agents are learning as they go?

Your API isn’t just for humans anymore. It’s now part of the infrastructure for agent-based systems.

Architecting the Next Generation of Autonomous Systems

Agentic AI systems are evolving from stateless prompt responders into autonomous entities that interact with data persistently and contextually. The key enabler: databases, because now they’re supporting long-term memory, state tracking, and goal progression. Architectures are moving towards combining LLMs with vector stores, graph databases, and traditional transactional systems to allow agents to reason over time, recall prior interactions, and take context-aware actions. Now it’s possible to build systems that think and act across sessions, rather than single prompt chatbots.

We’re at an inflection point where building GenAI features isn’t enough. Organizations are starting to architect true agents. That requires a rethink of storage, memory, and data interfaces. We’ve seen LLM projects stall because they lack persistence and continuity, because agents that forget are agents that fail.

LLMs gave us language. Databases will give them memory. Building agents without persistent state will mean you’ll just end up chaining prompts.

Data Literacy in 2025 Isn’t About Dashboards Anymore

Traditionally, data literacy has been focused on reading charts, interpreting KPIs, and navigating dashboards. But in 2025, the new version of data literacy is asking the right questions, understanding how AI systems derive answers, and making judgment calls when LLMs or automated insights present conflicting outputs. The tools themselves are less important, which means critical thinking, context awareness, and decision fluency is taking centerstage in a world where data is everywhere but often abstracted.

Organizations can over-invest in dashboard training and underinvest in actual decision-making capability. With AI copilots, chat interfaces, and natural language querying on the rise, business users don’t necessarily need SQL. But what they do need to know is when to trust an answer, challenge an assumption, or escalate a gap. This is especially important for leaders thinking about enablement, change management, and org readiness in an AI-first world.

The bottom line? Your teams need to be trained to interpret answers, not fetch them.

Blog Spotlight: Unlocking Impact with a Fractional Data Team

Hiring a full in-house data team isn’t always realistic, especially for mid-market companies trying to move fast without burning headcount. This post breaks down how a fractional data team can deliver high-impact outcomes with lower risk and faster time-to-value. From building modern data stacks to launching AI pilots, you can get senior-level execution without the long ramp or full-time commitment. If you're stuck between DIY and over-hiring, this is the model to explore.

What topics interest you most in AI & Data?

We’d love your input to help us better understand your needs and prioritize the topics that matter most to you in future newsletters.

Login or Subscribe to participate in polls.

“Data is the language of the powerholders.”

― Jodi Petersen