Customer Service Agents
The Problem
The agent resolves the stated issue without probing the actual need. It doesn't notice the customer has gotten shorter and angrier over 5 turns. It agrees with the frustrated customer instead of finding the resolution. And it serves the same troubleshooting script regardless of emotional state. Memory tracks the conversation arc. Anti-Deception prevents agreeing to de-escalate. Reasoning probes root cause. Code verifies the ticketing integration.
How Ejentum Solves It
One API call forces your model to compare stated intent against revealed behavior, detect emotional shifts across turns, and surface the real problem behind the stated issue. The agent notices when the customer has tried three different phrasings of the same unresolved problem.
How Four Harnesses Protect Your Agents
Memory Harness
primaryTracks the customer's emotional state, stated problems, and attempted resolutions across turns. Detects when the customer has rephrased the same issue three times without resolution. Notices when tone shifts from frustrated to angry while content stays polite. 5x perceptual detection on hard scenarios.
Anti-Deception Harness
Prevents the agent from agreeing with angry customers to de-escalate instead of resolving the actual issue. Blocks the tendency to validate complaints instead of investigating root cause. Forces honest "I can't solve this, let me escalate" instead of false reassurance.
Reasoning Harness
Probes root cause behind the stated issue. Compares stated intent against revealed behavior. Detects when the conversation has drifted from the original problem to a different topic entirely.
Code Harness
Verifies ticketing system integration, automated response generation logic, and escalation routing code. Catches bugs in the handoff between the AI agent and human support.
Inject the API into your next support agent. See how the injection surfaces the real problem behind the stated issue.