Softcery | Production AI Systems

Voice agents depend on multiple external services - speech-to-text, language models, and text-to-speech providers. Each service can experience outages or performance degradation. A recent 6-hour outage with a primary text-to-speech provider left a client's voice agent unable to respond to customer calls. Manual switching to a backup provider restored service. With 99.9% uptime per component, combined system reliability drops to approximately 99.7%. This creates enough downtime to impact customer experience. Effective redundancy requires multiple layers: ◾𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐒𝐓𝐓 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐫𝐬 𝐰𝐢𝐭𝐡 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐟𝐚𝐢𝐥𝐨𝐯𝐞𝐫 ◾𝐁𝐚𝐜𝐤𝐮𝐩 𝐓𝐓𝐒 𝐦𝐨𝐝𝐞𝐥𝐬 𝐫𝐞𝐚𝐝𝐲 𝐟𝐨𝐫 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐚𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧 ◾𝐇𝐞𝐚𝐥𝐭𝐡 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐭𝐡𝐚𝐭 𝐝𝐞𝐭𝐞𝐜𝐭𝐬 𝐢𝐬𝐬𝐮𝐞𝐬 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞𝐲 𝐚𝐟𝐟𝐞𝐜𝐭 𝐮𝐬𝐞𝐫𝐬 ◾𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐟𝐚𝐥𝐥𝐛𝐚𝐜𝐤𝐬 𝐟𝐨𝐫 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐝𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭𝐢𝐞𝐬 Training agents to manage service interruptions smoothly helps maintain customer satisfaction. Phrases like "Let me connect you with someone who can help" work when systems underperform. Customers expect consistent service. Technical issues handled transparently and resolved quickly tend to preserve customer patience. Voice agents serve as customer-facing systems requiring enterprise-level reliability planning. Multiple backup options ensure service remains available during critical moments. Most companies discover the importance of fallback strategies after experiencing their first major outage. Building redundancy from the start prevents service disruptions that damage customer relationships.

Agentic coding uses AI assistants as active participants in development under strict human oversight. Most teams treat coding agents like magic autocomplete. They're not. Treat agents as supervised assistants under strict rules. This article is a distillation of the most effective principles and workflows our team has adopted for software development. These practices are the product of extensive experimentation and are actively used to enhance our engineering efficiency. https://lnkd.in/dg3AuFGt

Building a voice agent is one thing. Making sure it actually works when users call is another. Voice agents have multiple components - speech recognition, language processing, business logic, voice synthesis - and each can fail independently. A systematic testing approach prevents most production headaches. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐭𝐞𝐬𝐭𝐢𝐧𝐠 𝐜𝐨𝐦𝐞𝐬 𝐟𝐢𝐫𝐬𝐭. Before touching the voice, test the LLM prompts as text. Write out conversations the agent should handle - appointment booking, order status, billing questions. Include edge cases: users saying "I don't know," partial information, profanity. More importantly, test tool calling. Does the agent invoke the CRM lookup at the right moment? Does it correctly format the appointment booking request? Most voice agents that fail do so because they can't reliably interact with external systems, not because they can't talk. 𝐀𝐝𝐝 𝐯𝐨𝐢𝐜𝐞 𝐭𝐞𝐬𝐭𝐢𝐧𝐠. Once scripts work, test the same conversations through actual phone calls. Speech recognition adds new failure modes - background noise, accents, poor connections. What works as text might break as audio. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐭𝐫𝐚𝐧𝐬𝐜𝐫𝐢𝐩𝐭 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧. Set up a secondary LLM to review every call transcript. Did the agent follow the business rules? Did it hallucinate information? Did it handle the edge case correctly? Running a simple prompt that scores accuracy, tone appropriateness, and task completion catches problems before customers complain. 𝐖𝐚𝐭𝐜𝐡 𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐢𝐞𝐬. Voice agents rely on multiple external services - STT providers, LLM APIs, TTS services. Each has downtime. Set up monitoring and fallbacks, or the agent goes down when they do. 𝐓𝐞𝐬𝐭 𝐚𝐠𝐞𝐧𝐭 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲. Make automated test calls every 15 minutes. Simple healthcheck to ensure the full pipeline is working, not just that the server is up. 𝐓𝐫𝐚𝐜𝐤 𝐜𝐚𝐥𝐥 𝐦𝐞𝐭𝐫𝐢𝐜𝐬. Call duration, hangup rates, transfer requests. If the average call deviations 10-20 % - something's wrong. If the hangup rate exceeds 15%, users are frustrated. These metrics reveal problems transcript review might miss. The testing pyramid for voice agents is inverted - more integration testing than unit testing needed because the interactions between components matter more than individual parts. Most teams skimp on testing because voice agents feel simple - just a conversation. The complexity is hidden in all the systems that make that conversation possible.

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