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04 / AI NATIVE ENGINEERING08 core services

AI Native Engineering Systems

Connect scenarios, knowledge, models, tools, agents, delivery, and runtime governance into a composable and continuously evolving enterprise AI engineering system.

AI foundationKnowledge engineeringModelOpsAgentOpsAI DevOpsEvaluation

Enterprise challenges

Move AI from fragmented pilots into a scalable engineering system

01

Scenarios and platforms disconnect

Pilots revolve around isolated tools without priorities, accountability, or a path to scale.

02

AI assets remain fragmented

Knowledge, models, prompts, tools, workflows, and agents are hard to discover, reuse, and govern.

03

A gap separates pilots from production

Experiments, evaluation, release, and runtime do not form one continuous engineering path.

04

Quality, safety, and cost stay opaque

Offline and online evaluation, traces, access, audit, and cost operations are incomplete.

AI engineering value chain

Connect scenario definition to continuous operations

  1. 01Scenario design

    Define value, priorities, human accountability, and evidence boundaries.

  2. 02Knowledge engineering

    Turn enterprise data into authorized, cited, and evaluated knowledge services.

  3. 03Model engineering

    Unify model selection, experiments, registry, evaluation, release, and operation.

  4. 04Agent engineering

    Compose models, memory, knowledge, tools, and workflows for complex work.

  5. 05Delivery and runtime

    Connect engineering, infrastructure, release gates, and runtime observability.

  6. 06Evaluation and governance

    Improve continuously through quality, safety, cost, and audit evidence.

Service work packages

Eight services form the enterprise AI foundation and engineering system

01

AI-native architecture and scenario design

Identify scenarios and design human-AI collaboration, target architecture, capability maps, and evolution paths.

Engineering role
Place models, knowledge, tools, and agents within explicit business responsibility and evidence boundaries.
Outcome
A scenario-driven AI transformation blueprint that can be delivered in stages.
Deliverables
AI-native architecture blueprint · Scenario portfolio and roadmap
02

AI resource and runtime systems

Build model services, gateways, inference, vector databases, and management of knowledge, prompts, and tools.

Engineering role
Host application-level AI services separately from the Kubernetes, GPU, and infrastructure runtime foundation.
Outcome
Discoverable, callable, and observable AI runtime resources.
Deliverables
AI resource and runtime architecture · Model, vector, and tool services
03

Enterprise knowledge engineering and RAG systems

Build ingestion, processing, indexing, retrieval, access, evaluation, and operating loops.

Engineering role
Connect models to authorized enterprise knowledge with traceable evidence citations.
Outcome
Reusable and evaluable knowledge services with continuous operations.
Deliverables
Knowledge engineering architecture · RAG pipelines and evaluation sets
04

ModelOps systems

Build model registration, versioning, experiments, evaluation, release, observability, and rollback.

Engineering role
Govern model lifecycles through reproducible experiments and evaluation evidence.
Outcome
Traceable model delivery from development to production operation.
Deliverables
ModelOps workflows · Model registry, evaluation, and release mechanisms
05

AgentOps systems

Build agent assets, orchestration, debugging, evaluation, observability, access, and audit.

Engineering role
Trace model calls, retrieval, tool use, and human approval across agent execution.
Outcome
Agents become evaluable and operable engineering objects.
Deliverables
AgentOps workflows · Agent evaluation, observability, and safety configuration
06

AI DevOps and engineering systems

Integrate AI coding, testing, review, knowledge, and automation tools into engineering workflows.

Engineering role
Provide governed developer assistance while tests, reviews, and release gates verify output.
Outcome
A better engineering experience without sacrificing quality or traceability.
Deliverables
AI DevOps usage standard · AI engineering tools and measurement mechanism
07

AI application evaluation, safety, and governance

Build datasets, automated and human evaluation, red teaming, access, audit, safety, and cost governance.

Engineering role
Create an evidence chain from offline validation to online observability for models, RAG, and agents.
Outcome
Reviewable evidence for launch and continuous AI operations.
Deliverables
AI evaluation and governance framework · Evaluation sets, safety baseline, and audit reports
08

AI engineering platforms and developer toolchains

Build a unified AI portal, workspaces, asset catalogs, templates, integrations, and self-service.

Engineering role
Package knowledge, models, prompts, tools, and agents as composable engineering assets.
Outcome
Lower AI engineering overhead and improve asset reuse.
Deliverables
AI engineering platform · Developer toolchains, asset catalogs, and templates

Reusable AI assets

Turn AI capability into discoverable, composable, governed assets

One asset catalog and engineering context

Connect every asset through versions, access, dependencies, evaluation, and runtime evidence so teams reuse capabilities instead of rebuilding them.

01

Knowledge

Documents, data, terminology, graphs, and access context

02

Models

Foundation, domain, embedding, and reranking models

03

Prompts

Templates, variables, versions, test sets, and policies

04

Workflows

Task decomposition, orchestration, state, and human approval

05

APIs

Business and data services with standardized contracts

06

MCP

Tool discovery, context protocol, and controlled service connections

07

Skills

Reusable task instructions, tools, and validation rules

08

Agents

Roles, memory, knowledge, tools, plans, and behavior policies

AI engineering operations

Eight operating systems govern the complete AI application lifecycle

01

KnowledgeOps

Operate knowledge ingestion, processing, access, quality, versions, and feedback.

02

ModelOps

Operate model experiments, registry, evaluation, release, observation, and retirement.

03

PromptOps

Operate prompt assets, versions, tests, outcomes, and applicability.

04

WorkflowOps

Operate definitions, state, failures, compensation, and approvals.

05

AgentOps

Operate agent assets, orchestration, evaluation, observability, access, and audit.

06

EvaluationOps

Continuously manage datasets, metrics, baselines, regression, and online feedback.

07

AI DevOps

Embed AI into demand, development, testing, review, and delivery.

08

SimulationOps

Use simulations, scenarios, and data to validate complex intelligent systems.

Core lifecycles

Govern models and agents while connecting evaluation, release, and runtime evidence

MODELOPS

Models from experiment to continuous improvement

  1. 01Experiment

    Record data, parameters, code, and environment

  2. 02Register

    Unify metadata, versions, and ownership

  3. 03Evaluate

    Validate quality, safety, performance, and cost

  4. 04Release

    Use approvals, rollout, and policy to enter production

  5. 05Observe

    Track calls, latency, quality, and drift

  6. 06Improve

    Roll back, retrain, replace, or retire

AGENTOPS

Agents from design to reliable operations

  1. 01Design

    Define roles, goals, and accountability

  2. 02Compose

    Connect models, knowledge, memory, tools, and skills

  3. 03Debug

    Replay task traces, state, and tool calls

  4. 04Evaluate

    Validate completion, evidence, safety, and cost

  5. 05Release

    Configure access, policy, approval, and runtime

  6. 06Observe

    Trace inference, retrieval, tools, and human steps

  7. 07Operate

    Update assets, rules, and behavior from feedback

AI engineering delivery

AI augments engineering while workflow and quality gates preserve accountability

  1. 01Demand

    Retrieve knowledge, clarify scope, and draft reviewable requirements

  2. 02Design

    Assist architecture, interface design, and impact analysis

  3. 03Develop

    Provide governed code, knowledge, and tool context

  4. 04Test

    Generate tests, expand boundaries, and analyze failures

  5. 05Review

    Explain changes, identify risk, and preserve human decisions

  6. 06Deliver

    Connect pipelines, quality gates, approval, and release

  7. 07Operate

    Connect incidents and feedback to engineering improvement

AI retrieves, analyzes, generates, and recommends; tests, reviews, approvals, and business acceptance still decide what enters production.

Evaluation, safety, and governance

Use verifiable evidence to control quality, risk, and operating cost

01

Quality evaluation

Offline, regression, and online evaluation for models, RAG, workflows, and agents.

02

Safety controls

Content safety, prompt protection, tool access, data boundaries, and red teaming.

03

Access and audit

Identity, roles, resource access, human approval, and complete operating records.

04

Runtime observability

Trace model calls, retrieval evidence, tool execution, tasks, and failures.

05

Cost operations

Analyze token, GPU, model, and scenario cost with budgets and quotas.

06

Release gates

Control launch through baselines, safety policy, business acceptance, and approval.

Enterprise AI foundation

Five layers connect workspaces, engineering, AI services, and runtime

  1. 01
    Unified AI foundation portal

    Role workspaces, scenario spaces, asset catalog, self-service, and operations views.

  2. 02
    AI engineering and agents

    Knowledge engineering, ModelOps, AgentOps, AI DevOps, evaluation, and simulation.

  3. 03
    AI capability services

    RAG, model services, workflows, prompts, APIs, MCP, skills, and tools.

  4. 04
    AI resources and runtime

    Kubernetes, GPU scheduling, distributed compute, inference, vector databases, and storage.

  5. 05
    Governance and integration

    Identity, safety policy, audit, observability, cost governance, and enterprise integration.

Implementation path

Start with high-value scenarios and grow platform, process, and internal capability

  1. 01Assessment and scenarios

    Inventory strategy, scenarios, data, talent, systems, and risk to set priorities.

  2. 02Minimum viable AI foundation

    Establish portal, knowledge, models, tools, and evaluation around priority scenarios.

  3. 03ModelOps and AgentOps pilots

    Validate lifecycles, workflows, platform, and governance in real work.

  4. 04Platform and scale

    Productize catalogs, templates, standards, and self-service for more teams and scenarios.

  5. 05Operations and transfer

    Build internal capability through FDE co-creation, training, metrics, reviews, and transfer.

Deliverables

Deliver the AI foundation and the engineering system that sustains it

01
AI strategy and architecture blueprint

Scenario portfolio, capability map, target architecture, principles, and roadmap.

02
AI resource and runtime foundation

CPU/GPU pools, container scheduling, model inference, data, and storage runtime design.

03
Knowledge engineering and RAG

Knowledge processing, retrieval, access, citations, quality, and continuous operations.

04
ModelOps and AgentOps systems

Assets, workflows, evaluation, release, observability, and operating loops for models and agents.

05
AI engineering portal and toolchain

Role workspaces, catalogs, templates, self-service, IDEs, and tool integration.

06
Evaluation, safety, and governance

Datasets, baselines, safety policy, access audit, cost, and release gates.

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