Agentic Automation Doesn’t Fail at Reasoning. It Fails at Inputs.

First, some basics: Working with enterprises moving from pilots and chat interfaces to production with apps, agents, and AI-driven processes that plan, act, and increasingly automate real work, one thing regularly missed is that the bottleneck has shifted. It’s no longer model capability. It’s whether those systems can assemble trusted, decision-grade context at execution time. In chatbots and answer oriented AI solutions, making content searchable and grounded is fundamental. So, why do AI agents and apps need more? Because … 

Search tolerates ambiguity. Automation does not.

An employee can scan results, ignore stale content and weak answers, navigate unclear decision rights, and compensate for missing context. An autonomous system can’t. When you embed AI decisioning into workflows (e.g., approving exceptions, triggering actions, routing work, or choosing among outcomes) inputs drive decision framing and bounds hallucination risks.

That’s why this next phase of Enterprise AI isn’t about faster models or bigger embeddings. It’s about how context is assembled, filtered, and governed as a workflow so that AI-driven solutions behave predictably in production.

This is also why a new class of platforms are emerging to help manage context, enabling the new class of “intelligent” applications, AI-driven process automation, and Agent builders like Elastic’s new Agent Builder. 

2025 was marked by POCs, but data and AI leaders are returning to a fundamental, but simple truism: 

The problem isn’t your outputs. It’s your inputs.

Why Agents and AI Apps Need More Than Search Retrieval

Search and retrieval-based AI works … until AI is asked to do work.

Early enterprise AI efforts focused on finding and summarizing information to support humans. That was a logical first step. Models answered questions they couldn’t before, accuracy improved, and teams saw quick wins.

Then teams try to operationalize.

That’s when new problems surface:

• Context becomes stale as source data changes
• Permissions break when data is copied or centralized
• Pipelines sprawl as teams stitch together retrievers, rerankers, tools, and frameworks
• Relevance degrades under multi-step workflows
• No one can reliably explain why a system produced a specific answer or action

These are not model failures. 
They are execution failures.

In practice, you’ll recognize these failures as delayed launches, failed security reviews, brittle integrations, and loss of organizational trust (and teams returning to manual options) once AI systems touch automated workflows that matter.

The reason: large language models (LLMs) produce an output based on the inputs given. Like the “sharp intern” example typically used, LLMs don’t know whether a document is outdated, what the formal/informal rules are, or whether the content is unauthorized or inappropriate for the current step.

What agents need is more than retrieval. They need fresh, permission-aware, constrained, and runtime-observable context as input to operate safely in production.

Vector Databases the Right Foundation … But Not the Decision Surface

Vector databases deserve credit. They made semantic search practical and helped enterprises ground large language models in proprietary data. For many organizations, they remain the right foundation for similarity-based retrieval.

But retrieval is not the same thing as providing the execution context that provides the input for LLM’s to reason and take actions that can be trusted.

Similarity alone can’t:

• Enforce freshness
• Respect permissions at runtime
• Apply business rules
• Account for workflow state forcing re-evaluation of data, allowable actions, and constraint changes mid-process
• Explain or audit why a decision was made … even as agents and humans need to reason, act, and revisit and override decisions

As AI systems become more stateful, iterative, and autonomous, these gaps surface quickly. What worked for search becomes an operational and governance blocker for automation.

This isn’t a critique of vector technology. It’s a required architectural shift. Vector databases optimize retrieval, which serves as the foundation for managing the context that enterprises are now trying to operationalize for agents and AI-driven decisioning.

What “Context” Actually Means in Production

Simply put, context engineering assembles the right information at execution time so AI systems can safely suggest or make decisions with the correct meaning, freshness, constraints, and governance.

It is not a replacement for catalogs, semantic layers, or vector search.
It is the operational layer that binds them together at execution time.

That said, one reason we hear teams struggle is that “context” gets treated as an abstract concept. Let’s make it more concrete.

From an operator’s perspective, context has three parts:

• Data: The right subset of information from across the enterprise (e.g., CRM, ERP, standard operating procedure, chat, etc.) that’s relevant, fresh, and scoped to the step being executed.
• Guidance: Intent, prompts, domain knowledge, and business logic that frame how the system should reason.
• Guardrails: Rules, permissions, constraints, and policies that define what the system is allowed to do.

Too little context leads to wrong decisions.
Too much context leads to hallucinations, cost overruns, and noise.

Hence, the goal isn’t “more data.” It’s just enough, structured correctly, at the moment of action.

Context Is a Workflow, Not Only a Query

Popular now are ideas like context engineering and management replacing search and retrieval pipelines, prompt engineering, hard-coded agent logic, and bespoke orchestration inside of app code.

Enterprise architects and data leaders are making the mental shift: context is not retrieved once but must be set up to be assembled at the time of decisions and actions.

In production systems, context moves to repeatable workflows:

• Retrieve candidate information
• Filter and discard low-quality results
• Enrich with metadata, semantic definitions, and/or state
• Validate permissions and authority
• Align to rules and intent
• Assemble the final execution context
• Observe and remember outcomes to feed corrections back and enable learning loops

This process happens across steps, across tools, and often across teams. 

Vector databases do not disappear in this model. Similarity search remains fundamental but becomes a key step in a larger decision pipeline that retrieves information, agent memories, and execution state. A pipeline where relevance is combined with rules, permissions, freshness checks, and auditability to produce the context required to make AI-driven processes reliable.

When context logic is buried inside application or agent code, systems become brittle and hard to govern. When context is treated as a first-class workflow, it becomes observable, controllable, and improvable.

Why Apps, Agents, and AI-Driven Processes Make This Shift Visible

This shift toward execution context isn’t limited to agent builders. You see it anywhere AI moves from “answering” to “acting”:

• AI-augmented applications that personalize, recommend, or guide users inside workflows
• Autonomous and semi-autonomous agents that plan, call tools, and iterate across steps
• AI-driven process automations that route work, approve exceptions, or trigger downstream actions

All of these struggle with the lack of the same thing: execution context inputs that are relevant, current, permission-aware, and aligned to business intent at the moment of action.

Any AI-driven automation, like agent builders, struggles with any miss of execution context and makes it obvious. When systems detect, decide, act, recover, and learn without a human correcting every step, the quality of context becomes the difference between automation and outage.

The Market Shift: Retrieval with Context Execution Layers 

What’s emerging in the market is a new platform convergence around context workflows and management, and not a one-time retrieval:

• Semantic layers and catalogs of data are moving closer to runtime, so meaning and policy can be enforced during execution
• Retrieval engines unifying data and signals from across the enterprise, combined with rules, filters, and constraints to return “just enough” context that is not only relevant, but allowed and current
• Agent runtimes are forcing platforms to manage memory, state, and tool use across multi-step workflows as governance and testing complexity spikes
• Observability shifting from offline reporting to runtime controls (trace, evaluate, intervene), to provide feedback loops and overrides.

Let’s make this shift more concrete by mapping the core execution context requirements to how platforms are addressing them in practice. Elastic provides an example of how vendors are quickly responding to these requirements; similar patterns are emerging across the market (Figure 1). 

Figure 1: Execution context requirements expanding operationalizing context.

In practice, this shift toward context execution is becoming visible through the convergence described above and through new platform capabilities emerging across the market. Elastic’s recent Agent Builder announcement provides one example of a vendor responding to the need. Rather than positioning agents as standalone intelligence, Elastic frames Elasticsearch as a context-engineering foundation where retrieval, security controls, execution state, and observability come together at runtime to support agents and applications.

The Agent builder framework itself is not the point. What it illustrates is that building production-grade agents requires a platform capable of dynamically assembling context, enforcing governance at the moment of action, and making behavior observable once systems are live. In this model, the hard problem is not making agents smarter, but ensuring they operate with an execution context that is relevant, governed, and controllable in production.

The important point for enterprise architects and leaders isn’t which vendor wins.

It’s that the architecture itself is changing.

MyPOV

We have entered the year of execution for enterprise AI. The early winners are no longer defined by model sophistication, but by their ability to control context at the moment decisions are made across applications, agents, and automated processes.

Enterprise AI efforts have already moved past prompt engineering, where teams manually tuned inputs to get better answers. The shift to context engineering introduced structured workflows that retrieve, filter, enrich, and constrain inputs so AI systems behave more reliably. This is where many leading teams are operating today.

What will make production AI real is the next transition: context management. Context must be treated as a governed, observable, repeatable capability rather than a one-off engineering task. This requires both expanded platforms and, importantly, a deliberate operating model that moves context ownership from individual developers to shared (and sometimes distributed ownership), managed infrastructure, and increasingly architected design.

Prompt engineering improves outputs.
Context engineering stabilizes execution.
Context management enables speed at scale.

As AI systems move from individual assistance and chatbots to action and automation, decision velocity will increasingly depend on execution context as input. The organizations that succeed will be those that treat context as a first-class operational asset, with clear ownership, controls, and accountability. 

Expect the volume of marketing and of noise around context to rise in 2026. The advantage will goto leaders who frame their requirements first, then deliberately layer context into your business environment rather than chasing tools or terminology.