As large language models (LLMs) have proliferated, so too has the cottage industry of “prompt engineering,” the practice of carefully crafting input prompts to coax desirable output from chatbots like GPT and Claude. Prompt engineering has become the default interface layer between humans and generative AI. But it’s also a bottleneck.

In contrast, agentic AI systems, like those developed at Paximal, point to a different paradigm entirely: context engineering. Rather than relying on one-off, handcrafted inputs, these systems are designed to autonomously operate within predefined workflows, leveraging deep context about the task, user, and domain. The difference isn’t just philosophical; it’s architectural. And it’s what enables agentic AI to move from toy to tool, from demo to deployment.

Prompt Engineering: A Friction Layer Disguised as Control

Prompt engineering emerged out of necessity. Early LLMs had no memory or autonomy, so users had to front-load every interaction with elaborate instructions, constraints, and clarifications. A well-crafted prompt became the only mechanism to nudge a stateless model toward consistent outputs.

But this approach quickly hits limitations:

• Fragility: Small changes in wording can produce wildly different outputs.
  
• Opacity: Trial-and-error replaces reproducibility and version control.
  
• Scalability: Prompt tweaking doesn’t scale when your goal is to automate hundreds of structured workflows.

Prompt engineering, in other words, is a user burden. It’s a workaround for the model’s lack of grounding. It assumes a user is present and willing to fiddle. That assumption breaks down in high-volume, high-stakes environments like patent drafting, regulatory compliance, or structured legal analysis.

Context Engineering: Structured Intelligence with Autonomous Agents

Agentic AI systems take a fundamentally different approach. Instead of asking a model to “guess well” based on a clever prompt, context engineering builds a scaffold around the model, explicitly defining the scope, structure, and sources of truth that guide the model’s decisions. In practice, this means:

• Stateful operation: Agents retain memory across tasks, building coherent narratives over long documents.
  
• Predefined goals and workflows: Outputs aren’t open-ended responses to one-off questions. Instead, they’re steps in a larger process with guardrails.
  
• Semantic context, not just syntactic cues: The system knows the purpose of each section, the relevance of each figure, and how user inputs align with drafting conventions or legal constraints.

At Paximal, for example, the system isn’t fed a single prompt asking it to “write a patent.” Instead, it operates as an orchestration of agents, each responsible for a specific part of the drafting pipeline, from claims to figures to background. Each agent has access to structured inputs, hierarchical task definitions, and alignment mechanisms with attorney users. This is context engineering in action: not one brilliant prompt, but a lattice of persistent structure, memory, and goals.

Crucially, context engineering at Paximal is a collaborative effort between the system and the attorney. Before drafting begins, attorneys work with the invention disclosure materials to ensure they are clearly explained and readily processable with clean descriptions, unambiguous figures, and consistent terminology. This isn’t just “garbage in, garbage out” prevention. It’s active alignment: shaping the input context so the agentic system can operate with precision, consistency, and legal fidelity.

Why It Matters: Alignment, Autonomy, and Adoption

Context engineering enables three things that prompt engineering simply cannot:

• Alignment with expert users: Agentic systems don’t replace attorneys; they align with them. By structuring context to reflect legal reasoning, the system produces work product that attorneys can review and validate, not rewrite from scratch.
  
• Autonomy at scale: Because agents operate with goals and memory, they can execute multi-step workflows without user micromanagement. This unlocks true productivity gains, not just novelty demos.
  
• Enterprise reliability: Structured context allows for versioning, testing, auditing, and iterative improvement, features necessary for real-world adoption in regulated or quality-sensitive domains.

From Prompt to Platform

Prompt engineering served us well as a bootstrapping tool. But it is no foundation for scalable automation. Just as web development moved beyond handcrafted HTML to frameworks and platforms, AI is moving beyond ad hoc prompts to architected systems.

‍

‍

‍

‍