Deterministic AI agents are emerging as a practical and reliable alternative to address the severe limitations and unreliability of fully autonomous, Gen AI agents in regulated, and high-stakes domains.
Gen AI agents are probabilistic by nature which makes them difficult to audit, certify, and defend. That’s a non-starter when decisions are high-stakes and must be explainable, repeatable, and compliant. Deterministic symbolic AI agents solve this by:
• Predictable outputs every time
• Explicit reasoning paths auditors can inspect
• Hard constraints enforced by design
• Versionable, testable logic that behaves like policy or code
• Known failure modes instead of hallucinations
Symbolic AI agents – Decision models vs Knowledge-graphs
For compliance, diagnostics, troubleshooting, advisory, and risk assessment workflows, symbolic decision models (decision trees, decision flows, tables, rules) outperform knowledge graphs because:
• Decision logic is explicit and executable, not buried in graph inference
• Every outcome map to clear, auditable conditions
• Gaps, and conflicts in the decision logic are visible and testable
• Changes can be reviewed and approved, not “rediscovered”
Knowledge graphs are good at discovering patterns and context in domains with large number of entities and complex relationships but are weak at complex decisioning.
The need for a hybrid agentic framework / ecosystem
The winning agentic architecture is a hybrid agentic AI framework combining:
• Symbolic AI anchors correctness, decision logic, control, and compliance
• GenAI handles natural language, flexibility, and UX
• Humans provide judgment, escalation, and accountability
The orchestration of the symbolic, GenAI, and human agents requires a new dynamic Decision Inference engine that can:
• Comply with the logic of the deterministic decision models (trees, tables, rules)
• Orchestrate the calling of Gen AI agents to capture decisioning attributes from natural language documents and communications.
• Accepts decision attributes volunteered by users or captured from documents, at any point during inference regardless of the order of the attributes appearing in the decision logic.
A seamless dynamic integration between decision powered inference and LLM powered conversational user interaction is critical to this.
• Intelligently call a human in the loop to capture attribute values or an asse