IICTI Certified AI Automation Engineer

Holders demonstrate that they can apply and evaluate automation opportunity assessment and value engineering, lLM agent architecture and orchestration, retrieval-augmented generation and enterprise knowledge integration and intelligent document processing and unstructured data automation, plus 7 further areas, in realistic professional scenarios, as defined in the AQSCERT scheme for AI Automation Engineer.

Who it is for: Professionals working, or preparing to work, in artificial intelligence roles.

1. 01Automation opportunity assessment and value engineering15% of the exam

   • Evaluate automation candidates and select the right mix of rules-based, ML and LLM-driven approaches by value and risk.
   • Scope: Process and task mining (Celonis, UiPath Process Mining), automation suitability scoring, deterministic vs probabilistic automation fit, ROI and total cost modelling including inference costs.
2. 02LLM agent architecture and orchestration6% of the exam

   • Architect agentic workflows with bounded autonomy, explicit state and predictable failure handling.
   • Scope: Tool calling and function schemas, Model Context Protocol (MCP), single vs multi-agent patterns, planning and reflection loops, state management, LangGraph and similar orchestration frameworks.
3. 03Retrieval-augmented generation and enterprise knowledge integration6% of the exam

   • Design retrieval pipelines that deliver accurate, access-controlled and traceable context to automated workflows.
   • Scope: Chunking and embedding strategy, hybrid search and reranking, vector database selection, permission-aware retrieval, grounding and citation, knowledge freshness and index governance.
4. 04Intelligent document processing and unstructured data automation7% of the exam

   • Design document automation that achieves measurable extraction accuracy with controlled exception handling.
   • Scope: Multimodal LLM extraction, OCR and layout models, schema-constrained structured outputs, confidence scoring, validation rules and exception queues for documents and email.
5. 05Hybrid automation: RPA, workflow engines and API integration20% of the exam

   • Architect end-to-end automations that combine AI components with durable workflow and integration layers.
   • Scope: RPA vs API-first integration, BPMN workflow engines (Camunda, Temporal), iPaaS and event-driven triggers, idempotent and durable execution, legacy system integration patterns.
6. 06Evaluation, testing and quality assurance of AI automations7% of the exam

   • Design evaluation frameworks that quantify automation quality and gate changes before production.
   • Scope: Golden datasets and offline evals, LLM-as-judge calibration, regression testing of prompts and models, task success metrics, A/B and shadow deployments, non-determinism management.
7. 07Human-in-the-loop design and exception management10% of the exam

   • Design human oversight points that keep high-risk decisions accountable without eroding automation value.
   • Scope: Confidence thresholds and escalation, approval gates for high-impact actions, reviewer UX, feedback capture for improvement, workload balancing and accountability models.
8. 08Security of agentic and LLM-based automation9% of the exam

   • Evaluate and mitigate security risks introduced when AI systems can read untrusted data and take actions.
   • Scope: OWASP Top 10 for LLM Applications, prompt injection and indirect injection, tool permission scoping and least privilege, data exfiltration risks, sandboxing, secrets handling and audit trails.
9. 09LLMOps, observability and cost-performance optimization8% of the exam

   • Lead the production operation of AI automations with controlled cost, latency and reliability.
   • Scope: Tracing of agent steps, token and latency budgets, model routing and fallback, caching and batching, prompt and model version management, drift monitoring and incident response.
10. 10AI governance, regulation and responsible automation6% of the exam

    • Design governance controls that keep automated decision-making lawful, explainable and auditable.
    • Scope: EU AI Act risk classification and obligations, ISO/IEC 42001 AI management system, NIST AI RMF, GDPR automated decision-making (Art. 22), bias assessment, model and data documentation.
11. 11Automation programme leadership and change management6% of the exam

    • Lead an enterprise automation programme that scales adoption while managing organizational change and risk.
    • Scope: Automation centre of excellence models, citizen developer governance, operating model and roles, workforce impact and reskilling, portfolio prioritization and benefits tracking.

Question types

Single choice 100%

Delivery

Online (remote proctored), Test centre, On-site (corporate)

Retakes

Up to three attempts within any 12-month period. A 14-day waiting period applies after a first unsuccessful attempt and 30 days after a second. Each retake requires a new booking and fee. Retakes draw a different randomised form from the question bank.

Provisional cut score — psychometric validation is required before live delivery. Blueprint v1.0.

1. 1

   Register

   Create a free account and start the programme.

2. 2

   Practice

   Online practice test (coming soon).

3. 3

   Training

   Optional

   Online course or classroom — or skip it.

4. 4

   Certification

   Independent exam and certification decision.

Training is optional and does not by itself confer certification. Find a training partner