Practices for embedding AI agents into enterprise systems [Dual-Track Verification] 💡 Don't "trust" LLM outputs -- verify them. Separating generation from verification into independent tracks catches hallucinations structurally. 🔥 Problems Solved - Factually incorrect information reaches customers or executives unchecked - Non-existent documents, clauses, or case law get cited (fabricated citations) - LLM miscalculations corrupt financial reports and estimates - Unsupported claims with no sources erode trust 🏗️ Proposed Pattern Set up a deterministic verification track independent from the agent's generation. Numeric verification re-calculates from trusted sources (DB/API) and cross-checks against agent output. Citation verification programmatically confirms that referenced documents exist and that quoted passages match the claims. Action verification pre-checks parameters against schemas and business rules. When mismatches are detected, the system rejects, retries, or escalates to a human. ✅ Selection Criteria - Fit: business domains where accuracy of numbers, facts, and citations is critical (finance, legal, analytics, support) - Not Fit: creative or subjective outputs where verification criteria cannot be defined ⚠️ Pitfalls - Low-accuracy verifiers generate excessive false positives that clog workflows - Pre-verification adds latency; for information-only outputs, consider post-verification instead - Verifying all outputs is cost-prohibitive; select verification targets based on risk level 🛠️ Implementation Approach 1. For numeric verification, build Python scripts that re-calculate values from trusted sources (Salesforce API, DB) and auto-compare against agent output 2. For citation verification (grounding), implement an embedding similarity check between RAG chunk search results and cited passages 3. For action verification, pre-validate output parameters against JSON schemas plus a business rule engine (e.g., OPA) 4. Wire mismatch handling into a Temporal workflow as "reject, retry (max 2), then escalate to human" 5. Set verification scope by risk level: full verification for finance/legal outputs, sampling-based verification for informational outputs #AIAgents# #EnterpriseArchitecture#