is there any way to implement something like this for guardrails with my livekit agent infra for my outbound agent? Either something livekit offers, or some kind of package we could build out own version of this without impacting agent latency We use elevenlabs currently for just the tts model, so not the entire elevenagent which would be required i think if we were to use their built in guardrails 2.0 ( I like the focus and medical advice guardrails) https://elevenlabs.io/blog/guardrails Guardrails 2.0: A redesigned control layer in ElevenAgents

Thanks for reaching out, Akash. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

How do you consider these pushbacks Here is a breakdown of the exact trade-offs between building this custom Parallel Guardrail architecture in your LiveKit environment versus switching to ElevenLabs’ fully hosted "ElevenAgents" to get Guardrails 2.0 out of the box. By comparing these, you can decide whether the engineering effort is worth the control it provides. 1. Latency vs. The "Slip" (The Execution Mode Trade-off) The Custom LiveKit Approach: Because we are using the parallel

before_tts_cb

interceptor, there is zero added latency to the Time-to-First-Byte (TTFB). However, because the guardrail LLM takes ~300-500ms to evaluate the sentence, the TTS will have already synthesized and played the first 1-3 words of the medical advice before

agent.interrupt()

cuts it off. • Note: This is actually the exact same trade-off ElevenLabs makes. Their documentation explicitly states: "You can run guardrails alongside the response for near-zero delay, though a fraction of a second of audio may play before interception." The ElevenLabs Approach: ElevenLabs gives you a toggle switch. You can either accept the "slip" (run alongside) or choose "Hold responses until fully cleared." If you hold the response, absolutely nothing slips through, but your agent will suffer a noticeable ~500ms+ latency penalty on every single turn while the guardrail runs. Building the "Hold" method in LiveKit is possible but breaks the streaming paradigm. 2. Control & Context Awareness The Custom LiveKit Approach (Winner): You have absolute, programmatic control. Because the guardrail is running inside your codebase, you can pass the patient's specific EMR data into the guardrail's prompt. For example, if the patient has hypertension, the guardrail can explicitly know: "Do not let the agent change the Lisinopril dosage." You also control the exact fallback phrasing dynamically based on the specific violation. The ElevenLabs Approach: You are relying on their generalized natural language rules. While you can define custom domain rules, the guardrail model is a black box. You cannot easily inject dynamic, per-patient database records into their background moderation models for every single call without complex API gymnastics. 3. Cost The Custom LiveKit Approach: You are making two LLM calls simultaneously. You pay for the primary Gemini 2.5 Flash model generating the response, and you pay for the secondary Gemini 2.5 Flash model evaluating the sentences. While Gemini Flash is exceptionally cheap, this still doubles your LLM token expenditure per conversation. The ElevenLabs Approach: ElevenLabs charges a premium per-minute rate for their end-to-end agent orchestration, which includes the guardrail compute. If you do high-volume outbound calling, paying ElevenLabs' premium agent rate (which bundles STT, LLM, TTS, and Guardrails) is typically much more expensive at scale than paying raw API costs to Deepgram, Google, and ElevenLabs (TTS only) via LiveKit. 4. Engineering Complexity & Maintenance The Custom LiveKit Approach: You own the code. You have to maintain the async buffer, tune the regex that detects sentence boundaries, and refine the guardrail LLM's prompt to minimize false positives. If the primary LLM streams weird punctuation, your buffer logic needs to handle it gracefully. The ElevenLabs Approach (Winner): It is a toggle in a UI. They have a dedicated AI Safety Engineering team continuously red-teaming and refining the models that power the Focus, Manipulation, and Content guardrails. You offload the maintenance entirely. 5. Vendor Lock-In The Custom LiveKit Approach: Your architecture remains heavily modular. If Anthropic releases a faster model tomorrow, you just update

Flex.llm_fallback

or swap the

guardrail_llm

. If Cartesia becomes better than ElevenLabs for TTS, you swap it. Your safety layer lives in LiveKit and works regardless of the underlying providers. The ElevenLabs Approach: To use Guardrails 2.0, you must use their entire orchestration layer (ElevenAgents). You give up the flexibility of LiveKit's deep integration with your backend (like your database cursors and custom tools) and become entirely reliant on ElevenLabs' webhook and tool-calling infrastructure. Summary Verdict If your priority is speed-to-market and zero maintenance, switching to ElevenAgents is the easier path. However, because you are building a deeply integrated outbound healthcare application (with complex AWS landing zones, CMS ACCESS models, and specific EMR integrations), giving up the LiveKit pipeline to use a black-box hosted agent is a massive architectural regression. Building the parallel interceptor keeps your latency at absolute zero while keeping you in full control of the patient data flow.

Thanks — this is a really thorough breakdown and you’ve already captured the core trade-offs clearly. I’ll acknowledge each of your points, confirm what’s accurate, add any important nuances, and give a recommendation tailored to an outbound healthcare agent with EMR integration. Quick acknowledgement • You’re comparing (A) a custom parallel guardrail in LiveKit (run alongside streaming TTS) vs (B) ElevenLabs ElevenAgents (hosted guardrails with the “hold” toggle). Your five axes (latency/slip, control/context, cost, engineering effort, vendor lock-in) are the right ones to weigh for this decision. Point-by-point confirmation and added nuance 1) Latency vs “slip” (execution mode) • Confirmation: Your description is correct. Running the guardrail in parallel (before_tts_cb style) yields near-zero added Time-to-First-Byte but allows a small “slip” (a few words) before an interrupt can stop playback. ElevenLabs documents the same trade-off. • Nuance: The only way to eliminate slip fully is to block streaming until the guardrail clears (the “hold” mode). That introduces the ~500ms+ latency on every turn you noted, and it changes the streaming user experience. Implementing “hold” in LiveKit is possible, but it will alter your streaming paradigm and user expectations (longer initial silence). • Practical option: Hybrid — stream immediately but synthesize with short initial phrases designed to be safe (e.g., prefatory, non-actionable statements), and gate or replace substantive content until the guardrail verdict arrives. This reduces harm from slip with minimal perceived latency. 2) Control & context awareness • Confirmation: You are correct that a LiveKit-hosted solution gives you stronger programmatic control and lets you inject dynamic, per-patient context (EMR constraints, medication blackout rules) into the guardrail prompt. That capability is critical for safe, personalized healthcare responses. • Nuance: ElevenLabs can support some custom rules, but it’s not the same as full runtime access to patient records and dynamic decision logic. For sensitive medical constraints, owning the guardrail inside your stack is a major advantage. 3) Cost • Confirmation: Your cost comparison is accurate in principle. Two simultaneous LLM calls double your LLM token usage vs a single model call, but ElevenLabs bundles compute and charges a packaged, per-minute rate that can be more expensive at scale. • Nuance: Real costs depend on the models chosen for the primary and guardrail calls. A smaller, cheaper classifier for the guardrail can reduce the added spend considerably. Also consider engineering cost (maintenance, red-team) as ongoing spend that tilts the cost-benefit. 4) Engineering complexity & maintenance • Confirmation: Building and operating your own guardrail requires maintaining buffers, sentence boundary heuristics, regex/heuristic lists, and guardrail prompt tuning. That’s non-trivial. • Nuance: You can reduce complexity with incremental rollout: start with prompt-only + simple rules, then add a small classifier for high-risk cases. You can also instrument false positives/negatives for continuous improvement rather than tackling everything up-front. 5) Vendor lock-in • Confirmation: Correct — owning the safety layer in LiveKit keeps you provider-agnostic and allows swapping LLM/TTS vendors without rewriting guardrail logic. ElevenAgents ties you to their orchestration. • Nuance: Depending on compliance needs (HIPAA), vendor lock-in isn’t only about flexibility; it’s also about whether you can validate the vendor’s controls. If ElevenLabs can meet your compliance and you prefer an offload, that’s still a viable path — but you lose fine-grained control. Recommendation for an outbound healthcare use case (practical, risk-aware) • If your top priorities are strict patient-safety guarantees and tight EMR integration (e.g., preventing medication dosage changes, mixing patient-specific constraints), build the guardrail inside your LiveKit pipeline. The ability to pass EMR constraints directly into the guardrail is critical and outweighs the engineering cost for clinical safety. • Use a hybrid architecture to balance UX and safety: a. Prompt-level guardrails (always): Strongly instruct the primary model to refuse/don’t provide medical advice beyond scope. b. Fast rule-based checks (always): Inline regex or keyword heuristics in

stt_node

/ early pipeline to catch PHI/PII and explicit high-risk requests cheaply and deterministically. c. Parallel lightweight classifier (on flagged turns): Run a small, fast moderation model locally or colocated; only escalate to the classifier when heuristics or prompt-confidence flags indicate a risk. This minimizes extra LLM calls/costs and keeps most turns zero-latency. d. Safe defaults on timeout: If the classifier times out, fallback to a safe refusal or scripted escalation message instead of streaming potentially harmful advice. e. Gradual rollout + continuous monitoring: Log guardrail decisions and false positives/negatives for iterative tuning and red-teaming. • If your priority instead is speed-to-market, minimal ops, and you can accept some loss of runtime EMR context or higher unit cost, ElevenAgents is legitimate — but it’s only advisable if their hosted guardrail can be driven by the specific dynamic rules you need (confirm their ability to ingest per-call context/EMR constraints in a secure way) and they meet your compliance requirements. Concrete next steps I can help with • If you want to build in LiveKit, I can produce a step-by-step implementation plan: ◦ Example guardrail prompt templates you can inject with EMR facts. ◦ Regex heuristics and sentence-boundary buffering patterns to reduce false slips. ◦ A minimal “judge” prompt for a small classifier that returns APPROPRIATE / INAPPROPRIATE. ◦ Suggested timeout and fallback UX behavior. • If you want to evaluate ElevenLabs further, I can list the exact integration questions to ask them (per-call context injection, hold-mode latency numbers, compliance attestations) so you can decide whether their hosted trade-offs are acceptable. Which direction would you like to explore next — a concrete LiveKit parallel-guardrail design (with code/prompt examples and buffer heuristics), or a checklist of questions to validate ElevenLabs for your EMR-integrated healthcare workflow? , with interactive elements