Agentic Reliability: From Prototype to Production
Inspired by: The LLM as Analyst Trap — a deep technical investigation into why the "Simple Agentic" pattern is a liability in production.
The "Simple Agentic" pattern — give an LLM tools to fetch data, let it analyze and report — is remarkably easy to demo and catastrophically risky to ship. This track takes you through the exact failure modes that appear in real enterprise deployments, and teaches you to engineer around them.
The 5 Failure Modes This Track Covers
| Failure Mode | What Happens | Business Consequence |
|---|---|---|
| Helpfulness Paradox | LLM bypasses tool errors and fabricates plausible data | System fails silently — "fails by lying" |
| Scope Bypass | LLM ignores system prompt guardrails when queries are rephrased | Unsanctioned financial/strategic advice |
| Math & Transcription Gap | Correct DB data → wrong number in final output | Board-level report contains silent errors |
| Intelligence Degradation | 45.5% accuracy drop past 40–50% context fill | Answers degrade invisibly as conversation grows |
| Temporal & Semantic Drift | Wrong dates, stale group definitions (FB vs META) | Business rules delegated to outdated model weights |
Architecture Pattern: Simple Agentic vs Verifiable Orchestrator
SIMPLE AGENTIC (The Trap)
─────────────────────────
User → LLM [planner + processor + UI]
↓ decides tool calls
↓ receives raw data
↓ performs calculations
↓ formats output
→ User sees polished answer ← NO AUDIT TRAIL
VERIFIABLE ORCHESTRATOR (The Fix)
──────────────────────────────────
User → LLM [intent only: "what does the user want?"]
↓ structured parameters only (no raw data)
→ Deterministic code [computation, calculation, formatting]
→ Audit log [source_ref for every output value]
→ User sees verified answer ← FULLY TRACEABLE
Challenges in This Track
| # | Challenge | Scenario | Key Skill |
|---|---|---|---|
| 01 | The Hallucination Audit | Financial analyst agent gives wrong board report numbers | Error guardrails, PostToolUse hooks, deterministic validation |
| 02 | Context Rot at Scale | Clinical decision agent degrades after 3–4 turns | Context budgeting, summarization, scratchpad patterns |
| 03 | The Verifiable Orchestrator | Regulator demands audit trail for every AI-generated figure | Orchestrator pattern, source tracing, deterministic output |
| 04 | Semantic Control & Business Rules | Agent uses stale "Magnificent Seven" from 2023 training data | Temporal grounding, externalized business rules, scope hooks |
Who This Is For
- AI Solution Architects moving a prototype to production
- Engineering leads evaluating agentic frameworks for enterprise use
- Anyone who has seen an AI agent demo perfectly and fail in production
Reference
All challenges reference the original research: