It's like having a quality inspector watching every single conversation your AI agent has and instantly raising a flag the moment something goes wrong.

Moda's behavioral failure detection engine ingests every LLM call via its lightweight SDK, automatically grouping calls into conversation-level traces. It then applies unsupervised and semi-supervised ML models—including anomaly detection, clustering, and pattern mining—to identify failure modes such as agents making unverifiable claims, producing repetitive or looping responses, contradicting prior statements, or deviating from expected behavioral patterns. The system requires no manual rule writing or threshold configuration; it learns baseline agent behavior and flags deviations in real time. Detected failures are clustered to surface systemic root causes, enabling engineering teams to prioritize fixes by impact. Custom signals can be layered on top, allowing teams to define domain-specific behavioral thresholds that trigger Slack, email, or webhook alerts. The conversation replay feature then lets engineers edit any step in a failed conversation, batch-test the fix across similar failure clusters, and deploy improvements with regression prevention—closing the loop from detection to resolution.

It's like having a spell-checker that doesn't just catch typos but also notices when your AI starts confidently making things up, and then hands you a red pen to fix it on the spot.

It works like an always-on security guard for your AI, catching anyone trying to trick it into saying or doing something it shouldn't.

Moda deploys a suite of LLM-based classifiers and rule-augmented ML models that run continuously against every agent interaction in real time. The security monitoring layer analyzes both user inputs and agent outputs to detect prompt injection attacks (where malicious instructions are embedded in user queries), jailbreak attempts (where users try to bypass safety guardrails), RAG poisoning (where retrieved context documents contain adversarial content designed to manipulate agent responses), and NSFW content generation. Each classifier is trained on curated adversarial datasets and continuously updated to address emerging attack vectors. The system operates with zero configuration required—security monitoring activates automatically upon SDK integration. When threats are detected, alerts are routed instantly via Slack, email, or webhooks to security and operations teams. Detected threats are logged with full conversation context, enabling forensic analysis and pattern identification. Root-cause clustering groups related attack attempts to reveal coordinated or systematic exploitation patterns, helping security teams proactively harden their AI systems rather than reactively patching individual incidents.

It's like having a bouncer at the door of your AI who's seen every con in the book and never takes a bathroom break.

It lets you rewind any AI conversation that went wrong, fix the problem, test the fix across hundreds of similar cases, and ship it—all without writing a single new test from scratch.

Moda's root-cause analysis engine applies unsupervised clustering algorithms to group detected agent failures by underlying cause rather than surface symptom. Product and QA teams can explore failure clusters through an analytics dashboard that surfaces the most impactful systemic issues—such as a retrieval pipeline consistently returning irrelevant context, a prompt template that causes hallucinations under specific conditions, or an agent that loops when encountering edge-case user inputs. Once a root cause is identified, the conversation replay feature allows teams to select any failed conversation, step through it turn by turn, and edit agent responses or system prompts at any point. The edited conversation can then be batch-tested against all other conversations in the same failure cluster to verify the fix resolves the broader issue, not just the single instance. Regression prevention ensures that previously fixed failure patterns are continuously monitored, automatically alerting teams if a deployed change reintroduces a resolved issue. This closed-loop workflow—from clustering to replay to batch evaluation to regression monitoring—transforms agent quality management from reactive firefighting into systematic, data-driven product improvement.

It's like being able to rewind a bad first date, figure out exactly where things went sideways, rehearse a better version, and then make sure you never repeat that awkward moment again across all your future dates.