Domain-integrated context engineering (DICE)

Domain-integrated context engineering (DICE) is a framework for embedding AI agents into software engineering environments by grounding them in domain context and aligning their behavior with company-specific workflows, ownership models, and golden paths.

Instead of giving agents instructions in isolation, DICE treats them like new employees. They are onboarded into your engineering environment with clear rules, ownership, and workflows. The result is predictable, repeatable outputs that follow specific organizational norms, rather than just generic industry standards.

Why DICE matters

DICE builds on the foundations of domain-driven design (DDD). Where DDD models the business domain, DICE connects those models directly to engineering metadata and operational statuses to provide agents with your full organizational context. This helps ensure the same ubiquitous language guides both humans and machines.

AI agents have to be treated as  first-class actors in the SDLC to operate effectively. They need the same clarity of ownership, resources, and workflows that developers rely on every day.

When agents lack domain context, they can contribute to agentic chaos — inconsistent outputs, misrouted incidents, or deployments that fail to match company conventions — and may even take destructive actions in an attempt to reach their goals. 

DICE prevents this by harmonizing humans and agents around the same domain model, which builds trust and reliability across the engineering organization. But it’s not a one-time setup: it’s a continuous practice. 

As teams evolve their services, ownership models, and workflows, they apply DICE to keep agents aligned with those changes. Each iteration refines how context is exposed, how golden paths are reinforced, and how agents make decisions inside those guardrails. Over time, DICE becomes part of the engineering rhythm: a routine exercise in updating models, validating behaviors, and retraining agents to reflect the current state of the domain.

By embedding agents into golden paths, reusable, proven pipelines for deployments, incident triage, and development tasks, DICE ensures that AI doesn’t just “follow instructions.” It learns how to operate properly inside the organization’s living systems, adapting predictably as those systems evolve.

Principles of domain-integrated context engineering

• Domain modeling involves representing services, environments, teams, and resources in a structured way, often through blueprints or CRDs.
  
  
• Context exposure means making ownership, policies, and workflows accessible to both humans and AI agents.
  
  
• Golden paths define proven pipelines that reduce friction and eliminate chaos.
  
  
• Reliability  replaces ad-hoc exploration with predictable tracks that lead to consistent outcomes — and when they don’t, provide audit trails that make it easy to find out what went wrong.
  
  
• Human-in-the-loop oversight ensures that governance and approvals are in place where risks are high.
  
  

How DICE works

DICE enforces bidirectionality: the same domain model that shapes inputs also validates outputs. Agents not only read domain context, they write back into it, ensuring responses stay consistent with business logic and system constraints. The DICE framework follows a clear cycle:

1. Map the domain: Build a unified model of services, ownership, and workflows.
   
   
2. Onboard agents: Equip AI agents such as Claude, Copilot, or Cursor with the necessary domain context.
   
   
3. Expose contextual tools: Provide self-service actions, policies, and metadata through blueprints or MCP integrations.
   
   
4. Guide through golden paths: Ensure that agents follow trusted pipelines for deployments, incidents, or reviews.
   
   
5. Iterate and govern: Update the model as the domain evolves, audit agent behavior, and enforce policies.
   
   

Benefits of DICE

Organizations that adopt DICE can expect:

• More predictable agent outputs that mirror company norms.
  
  
• Less engineering chaos, with fewer misrouted incidents and misaligned deployments.
  
  
• Faster onboarding for both agents and engineers.
  
  
• Stronger collaboration because humans and AI agents share the same context.
  
  
• Scalable adoption of AI across the SDLC without creating bottlenecks.
  
  

Challenges and limitations

While powerful, DICE also introduces challenges:

• Modeling the domain requires significant upfront effort. Consider using internal developer platforms or portals to harmonize and clearly define your domain.
  
  
• Too many CRDs or definitions can create operator sprawl and overwhelm teams.
  
  
• Domains evolve over time, which means models must stay current.
  
  
• Striking the right balance between governance and agility is difficult. Too much oversight slows workflows, while too little increases risk.
  
  

Example use cases

• Incident triage automation: Agents route issues to the correct owners, such as SREs or DevOps teams.
  
  
• Deployment pipelines: Agents carry out deployments by following the organization’s golden path conventions.
  
  
• Code review assistance: Agents evaluate pull requests against internal standards for naming, observability, and compliance.
  
  
• Developer self-service: Agents provision environments or resources using blueprints while respecting RBAC and policy requirements.
  
  

How Port enables DICE

Port makes DICE practical and scalable by providing:

• A unified domain model of services, owners, and workflows.
  
  
• Blueprints that capture schemas, ownership, dependencies, and semantics.
  
  
• MCP integrations that expose resources and self-service actions programmatically.
  
  
• Golden paths built into Port workflows, covering incidents, deployments, and service creation.
  
  
• Governance features such as RBAC, approval flows, and auditing of agent actions.
  
  
• A structured onboarding process for AI agents, giving them domain context and clear tracks to follow.
  
  

FAQs

How does DICE prevent agentic chaos?

By grounding AI agents in the company’s domain model and workflows, DICE ensures they act within predictable rules. This eliminates inconsistent outputs and misrouted tasks.

What are golden paths and why do they matter?

Golden paths are reusable pipelines that define the right way to handle tasks such as deployments or incident triage. By ensuring both humans and agents follow them, DICE guarantees consistency and reliability.

How does Port support onboarding AI agents?

Port provides a harmonized domain model, blueprints, and self-service workflows. These tools let organizations onboard AI agents just like new employees, equipping them with the context and guardrails they need to act predictably within the SDLC.

What challenges come with adopting DICE?

The biggest hurdles are the upfront investment in modeling the domain and the need to keep that model updated as workflows evolve. Organizations also need to carefully balance governance and agility to avoid bottlenecks or excessive risk.

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