Service / Agentic AI as a Service

Agent Evaluation & Assurance

Measure agent quality, policy compliance, tool-use safety and business outcomes before and after production release.

Business context

What this capability solves

Traditional software tests do not capture probabilistic reasoning and changing model behavior. Evaluation must combine deterministic checks, scenario sets, red-team cases and human quality review.

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.

Golden Task Sets

Representative business tasks, edge cases and expected outcomes.

Tool-Use Tests

Correct tool selection, parameters, action order and rejection of unsafe actions.

RAG / Knowledge Tests

Grounding, retrieval authorization, citations and stale-source handling.

Policy Tests

Data handling, approvals, forbidden actions and risk-tier controls.

Adversarial Tests

Prompt manipulation, malicious content and agent/tool abuse scenarios.

Regression Gates

Compare releases/model changes and block unacceptable quality or safety degradation.

Reference architecture

How the capability fits together

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

Business & Agent Portfolio

Use cases, agent owners, risk tiers, process boundaries and value hypotheses.

AAIOS Control Plane

Agent registry, orchestration, identity, policy, approvals, memory, tool/model gateways and evaluation.

Enterprise Execution

Models, RAG, APIs, SaaS, databases, workflows and sandboxed agent runtimes.

AgentOps

Observability, SLOs, cost, incident handling, kill switch, evidence and continuous optimization.

Controls & governance

Least-privilege agent/workload identity
Human approval for high-impact actions
Approved tool schemas and transaction validation
Data classification/DLP at model and tool boundaries
Memory retention and deletion policy
Comprehensive traces and action provenance
Evaluation gates before production
Kill switch, rollback and incident escalation

Priority use cases

Pre-production release gate
Model upgrade validation
Prompt/workflow change
Agent vendor change
Periodic assurance

Key deliverables

Evaluation framework
Golden datasets
Scenario library
Automated/human scorecards
Risk thresholds
Release recommendation
Regression dashboard

Integration considerations

AAIOS
Enterprise IAM/workload identity
APIs and SaaS systems
Data platform and RAG/vector stores
Workflow/BPM/ITSM
SIEM/SOAR and observability
GRC/evidence systems
Model endpoints/gateways

Implementation

Phased delivery

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

1. SelectPrioritize workflows by value, feasibility, risk and data/tool readiness.

2. EngineerDesign agents, tools, RAG, policy, human checkpoints and AAIOS runtime.

3. AssureSimulate, evaluate, red-team, approve and define SLOs.

4. OperateRun through AgentOps, manage incidents and continuously optimize.

Outcome and KPI framework

Task accuracyPolicy pass rateUnsafe action rateGrounding qualityRegression deltaHuman acceptance rate