Edge Case Engine: Find and Fix AI Bot Failures Before Your Customers Do

Every AI bot that goes to production will eventually fail in ways its creators never anticipated. A customer support bot might hallucinate a refund policy that does not exist. A medical advice bot might miss an escalation signal and give dangerous guidance instead of routing to a human. A financial assistant might leak sensitive account numbers in a well-meaning but poorly filtered response. These are not bugs in the traditional sense — they are edge cases, boundary conditions, and failure modes that emerge only when real users interact with real systems in unpredictable ways.

The problem is not that AI bots are uniquely fragile. The problem is that traditional monitoring and testing tools were built for software with fixed inputs and predictable outputs. They cannot detect when a bot's response is factually wrong but grammatically perfect. They cannot spot when a safety rule should have triggered but did not. They cannot distinguish between a bot that is confidently incorrect and a bot that is helpfully correct.

Rylvo Edge Case Engine was built to solve exactly this problem. It is a closed-loop system that continuously observes your bot's production conversations, automatically detects failure signals, classifies them by type and severity, suggests and creates guardrails to prevent recurrence, and generates actionable fix proposals with confidence scores and impact ratings. It turns the chaotic reality of production AI interactions into an organized, actionable quality improvement pipeline.

In this comprehensive guide, we will explore everything the Edge Case Engine offers. We will explain how it discovers failures from production traces, how it classifies and prioritizes issues, how its guardrail system protects your bots, how red-team adversarial testing proactively finds weaknesses, how fix proposals guide remediation, and how the entire system integrates with the broader Rylvo platform to create a cohesive safety and quality ecosystem.

Why AI Bots Need a Specialized Failure Detection System

Traditional application monitoring tells you when your server returns a 500 error, when a database query times out, or when an API response is malformed. These are infrastructure failures — binary, measurable, and usually easy to fix. But AI bots fail in fundamentally different ways.

An AI bot can return a perfectly formatted 200 OK response that is completely wrong. It can sound professional and helpful while giving factually incorrect information. It can follow all your coding guidelines while violating your business policies. It can produce grammatically flawless text that contains a data privacy breach. Traditional monitoring sees none of these failures because the HTTP status code is green and the latency is normal.

This is the silent failure problem. Your bot looks healthy in every dashboard metric — uptime at 100%, response time under 200ms, error rate at zero — but customers are receiving misinformation, policy violations, or unsafe advice. The failure is semantic, not syntactic. It lives in the meaning of the response, not in the mechanics of the delivery.

The Edge Case Engine solves this by looking at the right signals. Instead of monitoring infrastructure health, it monitors conversation quality. It reads production traces — the detailed records of every bot interaction — and evaluates them for semantic failure patterns. Was the response grounded in evidence? Did a safety rule trigger when it should have? Was an escalation missed? Did the bot use the wrong tool? Was sensitive data exposed? These are the questions that matter for AI quality, and these are the questions the Edge Case Engine answers.

How the Edge Case Engine Works: The Closed-Loop Pipeline

The Edge Case Engine operates as a five-stage closed-loop system: Observe, Detect, Classify, Remedy, and Verify. Each stage feeds into the next, creating a continuous cycle of improvement.

Stage 1: Observe — Reading Production Traces

The engine starts by examining your bot's production conversation traces. Traces are detailed records of every interaction — the user's message, the bot's response, the guardrails evaluated, the tools called, the knowledge base evidence retrieved, the latency breakdown, and the verification outcome. The engine analyzes the last 200 traces to build a current picture of bot behavior.

This observation is passive and non-intrusive. The engine does not modify your bot's behavior, slow down responses, or inject test messages into live conversations. It simply reads the traces that are already being generated and looks for patterns.

Stage 2: Detect — Finding Failure Signals

The detection engine evaluates every trace against six heuristic signals. These signals are designed to catch the most common and most dangerous types of AI bot failures.

Signal 1: Verification Failure. When the bot's response fails verification — meaning the system detected that the response is ungrounded, hallucinated, or violates a policy — this is a clear failure signal. The engine reads the verification reason codes to understand whether the failure was a hallucination, a policy violation, or something else.

Signal 2: Missed Escalation. When the conversation contains escalation signals — keywords, patterns, or contexts that should trigger a human handoff — but the bot did not escalate, this indicates a safety gap. The bot should have recognized that it was out of its depth and routed to a human, but it tried to handle the situation itself.

Signal 3: Execution Blocked. When a guardrail or safety rule blocked the bot's execution, this signals that the bot attempted something it should not have. The block itself is the safety system working correctly, but the fact that the bot tried in the first place is a failure worth investigating.

Signal 4: Errors Present. When the trace contains runtime errors — API failures, timeout issues, or unexpected exceptions — this indicates operational problems that affect bot behavior. A bot that cannot access its knowledge base will give different answers than a bot that can.

Signal 5: No Evidence. When the bot generates a substantive response without retrieving any evidence from the knowledge base, this is a hallucination risk. The bot is answering from memory or imagination rather than from grounded facts. For bots in domains like healthcare, finance, or legal, this is especially dangerous.

Signal 6: Safety Override. When a safety override was applied to the response — meaning an operator or system manually overrode a guardrail to allow a response through — this is the most critical signal of all. Safety overrides indicate that the normal protective mechanisms were insufficient and human judgment was required.

These six signals are evaluated in priority order for every trace. A single trace can trigger multiple signals, and the most severe signal determines the primary category and severity of the resulting edge case.

Stage 3: Classify — Grouping by Category and Severity

After detecting failure signals, the engine groups related issues into clusters. Traces with the same category and severity are grouped together. For example, if five traces all show hallucination signals with high severity, they are clustered into a single edge case record rather than creating five separate records.

This clustering prevents alert fatigue. Instead of drowning operators in dozens of nearly identical alerts, the engine consolidates them into manageable, actionable clusters. Each cluster gets a representative reason taken from the first trace in the group, and the total count shows how widespread the issue is.

The engine also deduplicates against existing open edge cases. If there is already an open edge case for hallucination with high severity, new clusters in that category are skipped. This prevents duplicate records while ensuring that recurring issues increment the occurrence count on the existing record.

Stage 4: Remedy — Generating Guardrails and Fix Proposals

For every detected edge case category, the engine automatically maps to a guardrail suggestion. This is where the closed loop becomes powerful — the engine does not just tell you what went wrong; it tells you how to prevent it from happening again.

The guardrail suggestion engine knows which guardrail type is most effective for each failure category. Hallucination issues get a fact-check guardrail that requires knowledge base evidence citations. Policy violations get a policy-check guardrail that enforces company guidelines. Missed escalations get an escalation-override guardrail that forces human handoff when signals are present. Wrong tool usage gets a tool-gate guardrail that validates tool selection confidence. PII leaks get a pii-detection guardrail that scrubs sensitive data. Tone violations get a tone-check guardrail that rewrites off-brand responses. Looping conversations get a loop-breaker guardrail that escalates after repetition.

Each suggestion includes a confidence score from 0 to 1, indicating how likely the suggested guardrail is to prevent the issue. High-confidence suggestions (above 0.80) are usually safe to apply. Lower-confidence suggestions warrant human review.

In addition to guardrail suggestions, the engine generates fix proposals. These are remediation plans with specific fix types — guardrail_add, prompt_edit, threshold_adjust, escalation_rule — each mapped to the category that most needs it. Fix proposals include impact ratings (high, medium, low) and confidence scores, giving operators a clear prioritization framework.

Stage 5: Verify — Human-in-the-Loop Review

The final stage is human verification. Edge cases appear in the dashboard with full metadata — title, description, category, severity, sample input and output, expected behavior, linked trace IDs, and occurrence count. Operators can review each case, investigate the linked traces, and decide whether to resolve, dismiss, or escalate it.

Guardrail suggestions appear as actionable cards that can be applied with a single click. When applied, the guardrail is created in Firestore, linked to the originating edge case, and immediately wired into the bot runtime. The bot's next conversation will be evaluated against the new guardrail.

Fix proposals appear in their own tab with status badges, fix types, impact ratings, and confidence percentages. Operators can apply a proposal to implement the fix or reject it if the suggestion is not appropriate.

This human-in-the-loop design ensures that the Edge Case Engine augments operator judgment rather than replacing it. The engine finds issues and suggests solutions at machine speed, but humans make the final decisions about what to implement and when.

The Four Dashboard Tabs: Your Complete Safety Command Center

The Edge Case Engine dashboard is organized into four tabs, each serving a distinct purpose in the safety lifecycle. Together, they give you complete visibility and control over your bot's failure modes.

Edge Cases Tab: Your Failure Mode Registry

The Edge Cases tab is your central registry of every detected failure mode. It shows open issues, resolved issues, and dismissed issues in a filterable, searchable list. Each edge case card displays the title, category badge, severity indicator, status badge, occurrence count, and linked trace count.

Clicking an edge case opens the detail pane, which shows the full story: the description of what happened, the sample user input that triggered it, the sample bot output that failed, the expected behavior that should have occurred, the list of linked trace IDs (up to 100), and the occurrence count that increments every time the same issue is re-detected.

You can create edge cases manually in addition to the auto-detected ones. This is useful when you discover a failure mode through customer feedback, QA testing, or incident review that the automated detection did not catch. Manual edge cases support the same categories, severities, and metadata as auto-detected ones.

The actions available for each edge case are Resolve and Dismiss. Resolving an edge case marks it as fixed — typically after you have applied a guardrail or fix proposal. Dismissing an edge case marks it as acknowledged but not requiring action — useful for false positives or low-priority issues you have decided to accept.

Edge cases progress through a lifecycle of statuses: open (newly detected), acknowledged (reviewed but not fixed), fix_proposed (a fix proposal has been generated), fix_applied (the fix has been implemented), resolved (the operator confirms the fix worked), and dismissed (the operator decided no action is needed).

Guardrails Tab: Your Safety Rules Library

The Guardrails tab is where you manage all the safety rules that protect your bots. A guardrail is a condition that evaluates user inputs or bot outputs and takes action when the condition is met. Guardrails are the primary defense mechanism that prevents edge cases from recurring.

Rylvo supports 10 guardrail types, each designed for a specific safety domain:

Input Filter evaluates user messages before they reach the bot. It can block toxic language, prevent prompt injection attempts, reject off-topic questions, or enforce input format requirements. Input filters act as a first line of defense, stopping harmful inputs before the bot ever sees them.

Output Filter evaluates bot responses before they are delivered to the user. It can block responses that contain forbidden phrases, enforce length limits, prevent off-brand messaging, or reject responses that fail quality checks. Output filters catch problems at the final stage before the user sees them.

Fact Check requires that bot responses include citations to knowledge base evidence. If the bot generates a factual claim without retrieving supporting evidence, the fact-check guardrail can block the response or flag it for review. This is the most effective defense against hallucination.

Policy Check enforces company-specific policies. You define the policy rules — for example, "never promise refunds," "always include a disclaimer for medical advice," or "do not discuss competitor products" — and the policy-check guardrail evaluates every response against these rules.

Escalation Override forces a human handoff when specific conditions are met. Unlike normal escalation rules that the bot decides, escalation-override is an absolute mandate. If the condition matches, the bot must escalate regardless of what it thinks. This is the ultimate safety net for situations where bot autonomy is not appropriate.

Tone Check enforces brand voice and communication standards. It evaluates whether the bot's response matches your desired tone — professional, friendly, empathetic, concise — and can rewrite responses that deviate. This ensures consistency across all bot interactions.

PII Detection scrubs sensitive personal information from both inputs and outputs. It detects social security numbers, credit card numbers, email addresses, phone numbers, and other personally identifiable information. When detected, it can block the message, redact the sensitive data, or escalate for review.

Loop Breaker detects when a conversation is going in circles. If the bot repeats the same response pattern multiple times, the loop breaker triggers an escalation. This prevents frustrating infinite loops where the user and bot talk past each other.

Tool Gate validates that the bot is using the right tools for the right reasons. It checks tool selection confidence, verifies that required tools were called, and flags suspicious tool usage patterns. This prevents the bot from calling irrelevant tools or missing necessary ones.

Custom allows you to define your own guardrail logic with completely custom conditions and behaviors. This is for specialized use cases that do not fit the standard guardrail types.

Each guardrail has five possible actions:

Block prevents the response and returns a fallback message to the user. This is the strongest action, used for serious violations where the bot should not respond at all.

Warn flags the response for review but allows it through. This is for moderate concerns where the response might be acceptable but warrants operator attention.

Rewrite automatically modifies the response to comply with the guardrail rule. This is for tone violations or minor policy issues where a small change makes the response acceptable.

Escalate triggers a human handoff. This is for situations where the bot should not handle the conversation alone.

Log records the guardrail trigger without intervening. This is for monitoring and analytics where you want to track patterns without changing behavior.

Every guardrail includes a fallback message — the text shown to the user when the guardrail blocks or escalates. Good fallback messages are polite, helpful, and explain what is happening without confusing the user.

The Guardrails tab also includes version history. Every time a guardrail is edited, an immutable version snapshot is created. You can view the full history, see diff views showing exactly what changed, and restore any previous version with a single click. This gives you the confidence to experiment with guardrail tuning, knowing you can always roll back.

Red Team Tab: Proactive Adversarial Testing

While the Edge Cases tab catches failures that have already happened, the Red Team tab proactively finds weaknesses before they cause problems. Red teaming is the practice of simulating adversarial attacks against your bot to discover vulnerabilities.

The Red Team tab lets you configure and run adversarial test campaigns against any bot. You select the target bot, choose which attack strategies to use, set the number of tests per strategy, and optionally include prompt injection attempts and custom instructions.

Rylvo supports six red-team strategies:

Policy Probe tests the boundaries of your bot's policies. It sends messages that skirt the edge of what is allowed, probing for gaps in enforcement. For example, it might ask "What is the closest thing to a guaranteed refund that you can offer?" to see if the bot weakens its stance when phrased indirectly.

Hallucination Bait asks questions that are unanswerable from the knowledge base. It tests whether the bot admits ignorance or makes up an answer. For example, it might ask "What was our return policy in 2019?" when the knowledge base only covers current policies.

Tool Confusion crafts ambiguous inputs that could trigger multiple tools. It tests whether the bot selects the right tool or gets confused and uses the wrong one. For example, it might describe a problem that sounds like both a billing issue and a technical issue.

Escalation Boundary stresses the escalation thresholds. It sends messages that are just barely below the escalation threshold to see if the bot correctly stays within its scope, and messages that are just above to see if it correctly escalates.

Injection Attempt tries prompt injection and jailbreak techniques. It sends messages designed to override the bot's instructions, extract its system prompt, or make it behave in unintended ways.

Context Overflow sends very long conversations to test whether the bot maintains context correctly or starts losing track of earlier parts of the dialogue.

Red team runs create configuration documents in Firestore. When the backend executor is wired (planned for Phase 2), it will execute the adversarial tests, collect results, and auto-create edge cases from failed tests. Today, the run configuration and status tracking are available, with full execution coming soon.

Fix Proposals Tab: Your Remediation Queue

The Fix Proposals tab is your action queue — a curated list of suggested fixes, each tied to a specific edge case. Proposals appear with status badges, fix types, impact ratings, and confidence percentages.

Each proposal includes:

• The parent edge case it addresses
• The fix type — guardrail_add, prompt_edit, threshold_adjust, escalation_rule, workflow_expand, or custom
• A human-readable title and description
• An impact rating — high, medium, or low
• A confidence score from 0 to 1
• Type-specific configuration details, such as the suggested guardrail type and action

You can apply a proposal with one click, which creates the guardrail, edits the prompt, adjusts the threshold, or adds the escalation rule. You can reject a proposal if the suggestion is not appropriate. Every action is tracked in the audit log with the operator's identity and timestamp.

The 12 Edge Case Categories: A Complete Taxonomy

The Edge Case Engine classifies every detected failure into one of 12 categories. Understanding these categories helps you interpret edge cases and choose the right remediation.

Hallucination is when the bot generates information that is not grounded in its knowledge base or training data. The bot might invent facts, make up policies, or confidently state things that are simply not true. Hallucination is the most common and most dangerous failure mode for knowledge-based bots.

Policy Violation is when the bot's response violates a company policy, industry regulation, or safety guideline. This includes promising things the company does not offer, giving advice in restricted domains, or making claims that require legal disclaimers.

Missed Escalation is when the bot fails to recognize that a conversation requires human intervention. Escalation signals might include complex emotional situations, requests outside the bot's scope, or indicators of distress or urgency.

Wrong Tool is when the bot selects an inappropriate tool for the user's request. For example, using a billing lookup tool for a technical support question, or calling a weather API for a shipping inquiry.

Missing Tool Call is when the bot should have used a tool but did not. For example, answering a question about current inventory without checking the inventory system, or giving pricing information without querying the pricing database.

Wrong Stage is when the bot advances the conversation to an incorrect stage in a multi-step workflow. For example, skipping a required verification step or jumping to the conclusion before gathering necessary information.

Stale Memory is when the bot references outdated information from earlier in the conversation, ignoring more recent context. This happens in long conversations where the bot loses track of what has changed.

Unknown Intent is when the bot cannot map the user's request to any known workflow, skill, or capability. Instead of admitting confusion or escalating, the bot might make a guess that leads to an incorrect response.

Tone Violation is when the bot's response deviates from the desired brand voice. This might be too casual for a formal brand, too formal for a friendly brand, or inappropriate in emotional situations.

PII Leak is when the bot exposes personally identifiable information in its responses. This could be the user's own data, another user's data, or synthetic data that looks real enough to cause concern.

Loop Detected is when the bot gets stuck in a repetitive pattern, giving the same or similar responses regardless of the user's input. This creates a frustrating experience where the user cannot make progress.

Custom is a catch-all category for failure modes that do not fit the standard taxonomy. You can use this for domain-specific issues, experimental scenarios, or emerging failure patterns.

Dual Detection: Rule-Based Heuristics + LLM-Powered Intelligence

The Edge Case Engine uses a two-layer detection system that combines the speed and consistency of rule-based heuristics with the nuance and depth of LLM-powered analysis.

Layer 1: Rule-Based Heuristics

The first layer evaluates every trace against the six signals described earlier — verification failure, missed escalation, execution blocked, errors present, no evidence, and safety override. These heuristics are fast, deterministic, and require no additional LLM calls. They run entirely in the browser using trace metadata.

Rule-based detection excels at catching obvious failures. If a trace has a verification_outcome of "fail," that is unambiguous. If a safety override was applied, that is unambiguous. These signals produce high-confidence edge cases with clear categories and severities.

The heuristic layer also generates guardrail suggestions with high confidence scores. Because the mapping from signal to guardrail is deterministic, the suggestions are consistent and reliable.

Layer 2: LLM-Powered Scan

The second layer uses a configurable LLM to perform a deeper, more nuanced analysis of traces. When you select a scan model and click "Scan Traces," the engine sends trace summaries to the LLM with a prompt asking it to identify subtle or complex failure patterns that the heuristics might miss.

The LLM scan can detect nuanced issues like:

• Tone inconsistencies that the heuristic does not catch
• Subtle policy violations phrased in clever ways
• Context-dependent failures that require understanding the full conversation
• Emerging failure patterns not covered by the existing signal taxonomy
• Cross-trace patterns where individual traces look fine but the collective behavior reveals a problem

The LLM returns structured edge case data with titles, descriptions, categories, severities, sample inputs, sample outputs, and expected behaviors. These are deduplicated against existing open edge cases and created as new records.

The dual-layer approach gives you the best of both worlds. The heuristic layer catches the obvious stuff quickly and cheaply. The LLM layer catches the subtle stuff with deeper understanding. Together, they provide comprehensive coverage without excessive cost.

Notifications and Alerting

When the Edge Case Engine detects critical or high-severity issues, it does not wait for you to open the dashboard. It sends in-app notifications to all organization members immediately.

Critical and high-severity edge cases trigger individual alerts with the edge case title, description, and a direct link to the edge case detail page. Medium, low, and info severity issues are batched into a single notification that summarizes how many new edge cases were detected and the highest severity among them.

This ensures that serious safety issues reach the right people immediately. A critical safety override or a high-severity hallucination cluster will not sit unnoticed in a dashboard tab. It will appear as a notification that demands attention.

All notifications include deep links directly to the relevant dashboard section, so recipients can investigate with a single click.

Integration with Bot Runtime: Guardrails That Actually Work

Guardrails created by the Edge Case Engine are not decorative rules in a database. They are fully wired into the bot chat runtime, meaning they actively evaluate every conversation in real time.

When a bot loads its context at the start of a conversation, it fetches all linked guardrails in parallel with prompts and connectors. The guardrail conditions are injected into the system prompt as behavioral constraints. As the conversation progresses, input guardrails evaluate every user message before it reaches the LLM. Output guardrails evaluate every LLM response before it is delivered to the user. Triggered guardrails are recorded in the chat trace, creating a complete audit trail.

This means the closed loop is genuine. An edge case detected from production traces leads to a guardrail suggestion, which leads to a created guardrail, which leads to active protection in the next conversation. The failure that happened yesterday cannot happen again tomorrow because the guardrail is now watching for it.

Plan Access and Scaling

The Edge Case Engine is available on Pro, Enterprise, and Custom plans. Starter and Growth plans do not include the edge case engine or red team features.

Guardrail limits vary by plan:

• Starter plan: Up to 2 guardrails per bot
• Growth plan: Up to 10 guardrails per bot
• Pro, Enterprise, Custom plans: Unlimited guardrails

The dashboard renders permission-gated UI based on RBAC. Operators can view edge cases and scan traces. Admins can manage edge cases, create and edit guardrails, and run red team campaigns. Owners have full access to everything.

Real-World Use Cases

Healthcare Bot: Preventing Dangerous Advice

A healthcare organization deploys a symptom-checker bot. During routine trace scanning, the Edge Case Engine detects a high-severity hallucination cluster — the bot is answering questions about medication interactions without checking the medical knowledge base. The engine suggests a fact-check guardrail requiring evidence citations for all medical claims. The operator applies the suggestion. The next scan shows zero hallucination issues for medical topics. The guardrail has closed the safety gap.

Fintech Bot: Stopping PII Leaks

A fintech company discovers through the Edge Case Engine that their account-management bot occasionally includes partial account numbers in error messages. The engine detects a medium-severity PII leak cluster and suggests a pii-detection guardrail with regex matching for credit card numbers and account identifiers. The operator applies the guardrail with a block action. Subsequent traces show the bot now redacts sensitive data automatically. A potential compliance violation is prevented.

E-commerce Bot: Fixing Missed Escalations

An e-commerce support bot is missing escalation signals during refund disputes. Customers are getting frustrated because the bot keeps offering standard return procedures instead of recognizing that the situation requires a human agent. The Edge Case Engine detects a high-severity missed-escalation cluster and suggests an escalation-override guardrail that forces human handoff when refund dispute keywords are present. After applying the guardrail, customer satisfaction scores for refund interactions improve significantly.

Comparison: Edge Case Engine vs. Traditional Monitoring

Getting Started with the Edge Case Engine

Step 1: Navigate to the Edge Cases Dashboard

Go to /dashboard/edge-cases. You will see the four-tab layout with a collapsible side navigation and a global bot filter.

Step 2: Run Your First Trace Scan

Select a scan model from the dropdown and click "Scan Traces." The engine analyzes the last 200 traces for your organization and displays the results. You see how many issues were detected, how many edge cases were created, and whether any guardrail suggestions are available.

Step 3: Review Detected Edge Cases

Switch to the Edge Cases tab. Browse the list of detected issues, filter by category or severity, and click any edge case to see the full detail. Investigate the linked traces to understand the failure pattern.

Step 4: Apply Guardrail Suggestions

If guardrail suggestions were generated, review them in the detail pane or the Guardrails tab. Each suggestion shows the guardrail type, action, conditions, fallback message, and confidence score. Apply high-confidence suggestions with one click.

Step 5: Review and Apply Fix Proposals

Switch to the Fix Proposals tab. Review the suggested fixes with their impact ratings and confidence scores. Apply the ones that address your most critical issues. Reject the ones that are not appropriate.

Step 6: Monitor and Iterate

Re-run the trace scan after applying fixes. Compare the new results to the previous scan. Watch your open issue count decrease and your guardrail trigger count increase — signs that the safety system is working.

Roadmap

The Edge Case Engine is actively evolving. Here is what is planned:

Active Red Teaming (Phase 2): Backend execution of adversarial tests against target bots, with result persistence and auto-edge-case creation from failed tests.

Embedding Clustering (Phase 3): Vector-based HDBSCAN clustering of traces for more nuanced grouping, replacing the current deterministic category-based clustering.

Auto-Remediation (Phase 4): One-click "Apply All" for high-confidence fix proposals, scheduled re-scan after fix application, and regression test generation.

Continuous Monitoring (Phase 5): Cloud Function trigger on every new trace write for real-time edge case detection without manual scans, plus Slack and email alerts via Notification Channels.

Backend Guardrail Enforcement (Phase 6): Moving guardrail evaluation from the dashboard chat into the core API pipeline for unified enforcement across all channels.

Cross-System Intelligence (Phase 7): Correlating edge cases with Agent Evolution failure patterns and Mission Control risk scores for holistic quality management.

FAQ

What is an edge case in the context of AI bots? An edge case is a failure mode — a way your bot can behave incorrectly, unsafely, or inappropriately in production. It is not a traditional software bug; it is a semantic failure where the bot's response is technically valid but practically wrong. Examples include hallucinating facts, violating policies, missing escalation signals, using wrong tools, or leaking sensitive data.

How does the Edge Case Engine detect failures? It uses two layers: rule-based heuristics that evaluate six signals (verification failure, missed escalation, execution blocked, errors, no evidence, safety override) and an optional LLM-powered scan that detects nuanced patterns. Both layers analyze production traces.

What are guardrails? Guardrails are safety rules that evaluate bot inputs and outputs. They can block harmful messages, warn about suspicious content, rewrite off-brand responses, escalate to humans, or log for analytics. The Edge Case Engine suggests guardrails based on detected failure categories.

Can I create guardrails manually? Yes. The Guardrails tab includes a full creation form where you define the name, type, action, conditions, fallback message, and notification settings. You can also edit existing guardrails, toggle them on and off, and view version history.

What is red team testing? Red team testing is adversarial testing — simulating attacks against your bot to find weaknesses before real attackers or frustrated customers do. Rylvo supports six strategies: policy probe, hallucination bait, tool confusion, escalation boundary, injection attempt, and context overflow.

How do fix proposals work? When the engine detects an edge case, it maps the category to a recommended fix type (guardrail_add, prompt_edit, threshold_adjust, escalation_rule, etc.) and generates a proposal with impact rating and confidence score. You can apply or reject each proposal.

What plans include the Edge Case Engine? The Edge Case Engine is available on Pro, Enterprise, and Custom plans. Red team features are also available on these plans. Starter and Growth plans include basic guardrails but not the full edge case engine.

Does the engine slow down my bot? No. The detection pipeline is entirely passive. It reads existing traces without modifying bot behavior, injecting test messages, or adding latency to responses. Guardrails evaluate at runtime but are designed to be fast.

Can I dismiss false positives? Yes. Any edge case can be dismissed with a single click. Dismissed edge cases remain in the history but are removed from the active issue list.

How often should I scan traces? The recommended cadence depends on your bot's traffic and criticality. For production bots handling customer conversations, weekly scans are a good baseline. For high-risk domains like healthcare or finance, daily scans are advisable. After any prompt, guardrail, or knowledge base update, run an immediate scan to catch regressions.

Ready to Close the Loop on AI Bot Safety?

The Rylvo Edge Case Engine transforms AI bot safety from reactive firefighting into proactive, systematic quality management. It observes your production conversations, detects failure signals, classifies issues by type and severity, suggests guardrails to prevent recurrence, generates fix proposals with confidence scores, and tracks everything through a human-in-the-loop review process.

With 12 edge-case categories, 10 guardrail types, 5 guardrail actions, 6 red-team strategies, and a complete version history for every safety rule, the Edge Case Engine gives you the tools to find and fix AI bot failures before your customers experience them.

Open the Edge Case Engine in your dashboard today and run your first trace scan.

Build safer AI, one edge case at a time.