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🛠️ What is ElevenLabs’ v0 Agents Starter

ElevenLabs, in partnership with v0 (by Vercel), launched a template called “ElevenLabs Agents Starter”, meant as a boilerplate/framework for developers to build voice agents using Next.js. It provides a starting point — pre-set structure, UI, and integrations — so you don’t begin from scratch when creating custom conversational voice agents.

Why It Matters in the Bigger Picture

• Faster ramp-up for devs: By providing a starter kit, devs can prototype voice agent use cases more quickly — experiment, iterate, test — without having to wire together the front end, backend, voice SDKs, etc.

• Lower barrier to entry: Especially helpful for smaller teams, indie projects, or internal tools — those who want to try voice agents but don’t have deep voice-AI or infra expertise.

• Standardization & community sharing: Starter templates tend to propagate best practices. If many projects use this template, you get more reusable patterns, community feedback, and potential improvements (or forks) that benefit the ecosystem.

What to Watch Out For

• Template limitations: As with any starter kit, the v0 Agents Starter may impose unneeded constraints (architecture, tech stack, UI layout, etc.) that might need reworking as use cases get more complex.

• Voice-agent specifics: Voice behavior, latency, error-handling, turn-taking, interruptions, etc., are hard problems — using a starter template doesn’t eliminate these; careful tuning & testing are still essential.

• Updates & maintenance: Starters/templates may lag behind platform changes (APIs, SDKs, models). Developers should check how well maintained the v0 template is, and plan for updating their code in response to platform shifts.

🤝 How Top Sales Teams Are Using Agentic AI

According to a recent HBR piece, high-performing sales organizations are increasingly deploying agentic AI to bolster the sales engine — not just automate tasks, but actively anticipate buyer behavior, integrate insights across systems, and assist reps across the deal lifecycle (nurture, outreach, closing). These agents are doing more than responding; they’re planning the next move, adapting to changing conditions, and continually learning from sales feedback loops.

Why This Is a Big Shift

• Amplified seller capacity: Agentic AI lets teams replicate the best practices of top performers at scale. Every rep can be augmented by a smart companion that remembers what works.

• Better decision support: With AI pulling together CRM data, customer signals, market dynamics, these systems help in spotting risks/opportunities earlier, and advising on what move to make next.

• Continuous improvement built in: These setups aren’t static. They learn from what worked, what failed — optimizing messaging, follow-ups, and resource allocation over time.

What to Watch Out For

• Human buy-in & trust: Reps may resist or distrust AI suggestions, especially when agents act more autonomously. Transparency into how decisions are made and when AI is stepping in is key.

• Data quality & integration: Agentic AI is only as good as the data it sees. Poor CRM hygiene, missing cross-channel signals, or siloed tools can limit performance.

• Oversight & ethical guardrails: As agents take more initiative (like reaching out or making suggestions), there’s risk of mis-alignment (overpromising, privacy slipups, spamming). Clear control points and escalation paths are essential.

♟️ Intel’s ShashGuru: Agentic AI Analyzes Every Chess Move

Intel, together with the University of Bologna, unveiled ShashGuru — an agentic AI system that blends a fine-tuned version of a language model (Llama-3.1-8B) with a modified version of Stockfish (called ShashChess). It’s being used in the AI Analysis Room at the Alma Mater University Chess Tournament in Bologna. Post-game, players use ShashGuru to reflect on their games, uncover strategic ideas and optimize future moves. Spectators also get interactive insight — chat with the AI to understand key decisions, turning complex chess analysis into something accessible.

ShashGuru runs locally on Intel Core Ultra 200V PCs using OpenVINO, giving real-time feedback without sending data to the cloud; for viewer scale, it uses more powerful Xeon infrastructure.

Why It Matters

• Democratizing high-level analysis: Traditionally, deep chess analysis has required expert coaches or expensive compute. ShashGuru makes it possible for ordinary players and spectators to get grandmaster-level insights, improving the learning curve.

• On-device inference strength: Running locally (with Intel hardware + OpenVINO) reduces latency, improves privacy, and shows the increasing viability of powerful AI at the edge. It’s a model for other domains where instant analytical feedback matters (e.g. sports, education, strategy games).

• Conversational & explanatory interfaces: The ability to chat about why a move was good or bad, explore tactics, see alternatives — this pushes AI beyond just “move engine” into teaching and interpretability. That increases trust and usability.

What to Watch Out For

• Hardware & deployment constraints: On-device models bring benefits, but need powerful chips; performance will vary based on hardware. Not everyone can replicate this experience.

• Risk of “post-hoc justification”: When AI explains decisions after the fact, there’s a risk it frames analysis to sound convincing rather than strictly accurate or unbiased. Need safeguards to avoid misleading rationalizations.

• Generalization beyond chess: Techniques here are powerful in a deterministic domain like chess, where rules are fixed. Applying similar agentic AI in domains with fuzzier rules or uncertainty (negotiation, politics, creative work) will bring additional challenges.

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