Service / AI System Advisory & Development

Agentic AI Implementation

Design and deploy AI agents that can plan, retrieve, use tools and execute governed business workflows through controlled operating boundaries.

Business context

What this capability solves

Agentic AI introduces action risk: the system can change records, trigger transactions and coordinate multiple tools. Implementation must combine orchestration with identity, policy, evaluation and AgentOps.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Agent Design

Role, objective, boundaries, planning depth, memory and human interaction.

Tool Contract

Approved tools, schemas, permissions, transaction limits and error behavior.

Orchestration

Task graphs, agent specialization, hand-offs, retries and deterministic checkpoints.

Knowledge / Memory

RAG, short/long-term memory, authorization, retention and provenance.

Policy / Approval

Risk-based action gates, maker-checker, budgets, rate limits and kill switch.

AgentOps

Trace, evaluate, monitor, incident-manage and optimize agents after release.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Business & Governance

Business objectives, accountable owners, risk appetite, policy, use-case portfolio and investment priorities.

AI Engineering Lifecycle

Data, model, prompt/RAG, agent, evaluation, release, monitoring and retirement controls.

Trust & Assurance

Risk/impact assessment, security, privacy, explainability, human oversight, testing and evidence.

Enterprise Operations

Integration, observability, incident handling, change governance, model/agent lifecycle and continuous improvement.

Controls & governance

Named business and technical owner
Use-case risk classification and approval gates
Data provenance, minimization and access control
Human accountability for high-impact outcomes
Security and privacy-by-design controls
Versioned model/prompt/agent configuration
Pre-release evaluation and red-team gates
Continuous monitoring, incident and change control
Audit-ready evidence and management reporting

Priority use cases

Compliance operations agents
Customer-service resolution
IT operations
Finance/reconciliation
Research/knowledge workflows
Industrial maintenance assistant

Key deliverables

Agent architecture
Tool catalogue/contracts
Policy and approval matrix
Evaluation suite
AAIOS deployment configuration
AgentOps dashboard
Incident runbook

Integration considerations

Enterprise IAM and workload identity
Data lake/warehouse and vector/RAG platforms
Model/API providers and private models
Application/API integration layer
MLOps/LLMOps/AgentOps and observability
SIEM/SOAR and security tooling
GRC, privacy and evidence repositories
ITSM/BPM and business workflow systems

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. AssessInventory use cases, systems, stakeholders, data, risks, maturity and constraints.

2. DesignDefine target architecture, governance, controls, delivery backlog and acceptance criteria.

3. BuildDevelop or configure AI capabilities, integrations, controls, evaluation and operating procedures.

4. AssureTest quality, safety, security, privacy, explainability and business acceptance before release.

5. OperateMonitor outcomes, drift, incidents, changes, cost, risk and control effectiveness through BAU governance.

Outcome and KPI framework

Task successHuman escalationUnauthorized action blocksTool error rateCost per workflowAgent incident MTTR