To unlock agentic AI later, enterprises must deploy GenAI today with precision, governance, and reuse in mind.
GenAI is no longer experimental. It’s embedded in workflows, powering content generation, summarization, and automation across enterprise environments. But most deployments are short-sighted—built for isolated wins, not long-term scalability. That’s a problem.
Agentic AI—systems that act autonomously across tasks—will demand structured inputs, reusable components, and robust governance. If GenAI is deployed without these foundations, organizations will face rework, fragmentation, and poor ROI. Here’s how to avoid that trap.
1. Treat Prompts as Modular Assets
Most GenAI deployments rely on ad hoc prompting. Teams write one-off instructions, test outputs, and iterate manually. This works for isolated tasks but fails at scale. Prompts must be treated as reusable, modular assets—versioned, documented, and optimized for clarity and consistency.
Without prompt modularity, agentic systems cannot chain tasks reliably. Each prompt becomes a bottleneck, requiring human intervention. Enterprises that fail to standardize prompt libraries will struggle to orchestrate multi-step workflows later.
Build prompt libraries with clear naming, input/output expectations, and reuse patterns. Treat them like code.
2. Capture Context as Structured Metadata
GenAI outputs improve when context is clear. But most systems rely on implicit context—user memory, prior messages, or vague instructions. This limits portability and reuse. Agentic AI will require explicit, structured metadata: task type, audience, tone, constraints, and domain.
Without structured context, agentic systems cannot adapt prompts across use cases. For example, a summarization agent needs to know whether it’s summarizing for legal review, executive briefing, or customer support. That context must be machine-readable.
Design GenAI workflows to capture and store structured metadata alongside prompts and outputs.
3. Govern Output Quality with Clear Evaluation Criteria
GenAI outputs vary. Enterprises often rely on human reviewers to assess quality, but agentic systems won’t have that luxury. Evaluation criteria must be codified—clarity, completeness, tone, factual accuracy—and embedded into the workflow.
Without clear evaluation logic, agentic systems will propagate low-quality outputs. This creates downstream risk in decision-making, compliance, and customer experience. Enterprises must define what “good” looks like and enforce it consistently.
Establish scoring rubrics and automated checks for GenAI outputs. Don’t rely on subjective review.
4. Separate Task Logic from Domain Knowledge
Many GenAI deployments entangle task logic (e.g., “summarize this”) with domain-specific instructions (e.g., “summarize for a healthcare compliance officer”). This limits reuse. Agentic AI will require clean separation: task agents that know how to execute, and domain agents that know what matters.
In healthcare, for example, summarization tasks must respect privacy, terminology, and regulatory nuance. If that logic is hardcoded into every prompt, it cannot be reused across domains. Separation enables composability.
Design GenAI systems with layered logic—task execution separate from domain-specific constraints.
5. Avoid Hardcoding Business Rules into Prompts
It’s tempting to embed business rules directly into GenAI prompts. “Only include data from the last 30 days,” “Exclude internal-only content,” etc. But this creates brittle systems. As rules change, prompts must be rewritten—manually and repeatedly.
Agentic AI will require dynamic rule injection. Business logic should live in external systems—retrieved and applied at runtime. This enables agility and reduces maintenance overhead.
Store business rules in structured repositories and inject them into GenAI workflows dynamically.
6. Use Private Models for Sensitive Workloads
Public GenAI models are useful for general tasks, but they’re risky for sensitive workloads. Agentic systems will handle contracts, financial data, and internal strategy. These require private, auditable models with strict access controls.
Retail and CPG organizations, for instance, often generate product descriptions and campaign briefs using GenAI. If these include unreleased SKUs or pricing strategies, public model exposure introduces competitive risk.
Deploy private GenAI instances for any workload involving proprietary or regulated data.
7. Design for Agent Interoperability
Agentic AI is not one model—it’s a system of agents. Each agent handles a task: summarization, classification, routing, validation. If GenAI deployments are siloed, agents cannot collaborate. Interoperability requires shared standards for input/output formats, error handling, and task handoff.
Without interoperability, agentic systems become brittle. One agent fails, the chain breaks. Enterprises must design GenAI components with clear interfaces and fallback logic.
Define input/output schemas and error protocols for each GenAI task. Build for orchestration, not isolation.
GenAI is the foundation. Agentic AI is the future. Enterprises that build GenAI systems with modularity, governance, and reuse in mind will transition smoothly. Those that don’t will rebuild from scratch—at cost.
What’s one GenAI design principle you’ve prioritized to make future agentic AI easier to deploy? Examples: separating task logic from domain rules, building prompt libraries, or enforcing output evaluation criteria.