Decision Support Case Study
AI Business Agent
A validation-backed prototype combining retrieval, orchestration, and simulation-oriented assistant flows for business operators.
Mohamed Ali Bouzir designed a structured assistant workflow that separates factual evidence, contextual retrieval, and response composition for human-reviewed operational decisions.
Assistant framing
Evidence-first
Responses are composed from explicit evidence layers rather than direct free-form generation.
Operational mode
Human-reviewed
Recommendations remain advisory and are reviewed by an operator before action.
Workflow scope
Multi-route
Assistant can route between retrieval-heavy, analytics, and simulation-style paths.
Before → After Snapshot
Information access
Before: Manual report lookup and fragmented documents
After: Unified assistant workflow with traceable evidence context
Decision narrative
Before: Ad-hoc explanations without structured sourcing
After: Response composition tied to explicit retrieval and data layers
Operational confidence
Before: Opaque outputs and low traceability
After: Validation-backed prototype behavior with review checkpoints
Problem
Problem
Business users needed faster operational answers without sacrificing evidence traceability and review controls.
- Knowledge was spread across structured tables and semi-structured documents.
- Decision support required explainability for operational confidence.
- The workflow needed to remain advisory with human approval gates.
Constraints
Constraints
Accuracy posture and transparency were prioritized over automation volume.
Outputs required clear evidence context.
Response composition had to separate data and narrative responsibilities.
Operator review remained mandatory for sensitive decision paths.
Options
Options considered
Several alternatives were compared before final architecture selection.
Single-model direct answering without retrieval boundaries.
Rules-only approach without assistant composition.
Hybrid retrieval + orchestration + advisory response composition.
Why this approach
Why this approach worked
A layered assistant architecture aligned with evidence-first goals.
- Structured and contextual retrieval are handled separately.
- Assistant route selection keeps response paths explicit.
- Advisory outputs remain human-reviewed before operational use.
Architecture
Pipeline view
Retrieval, simulation, and guardrails combine to keep accuracy accountable while staying within the 3 second latency budget.
Operational resilience
Keeping trust during failure modes
Expected weak points were handled through guardrails and review loops.
Mitigations
- Low-confidence responses trigger clarification-oriented behavior.
- Missing context prompts retrieval fallback checks.
- Operator notes are fed back into refinement cycles.
Next steps
- Expand scenario coverage in validation scripts.
- Improve retrieval explainability for edge-case queries.
- Formalize feedback loops for stakeholder review sessions.
Contact
Ready to collaborate on AI-native products and resilient MLOps.
Email bouzirdali@gmail.com or call +216 56 815 716. The form on this page opens your email client with a prefilled draft for reliable contact.