← Back to Blog

Launch HN: Voker (YC S24) – Analytics for AI Agents

• AI agents, analytics, observability, YC, product development, LLM, monitoring, agent performance, AI tooling, startup

We're at an inflection point in AI product development. The conversation has shifted from "Can we build AI agents?" to "How do we make them reliably excellent?" And here's the uncomfortable truth: most teams are flying blind.

I've spent the last year building AI-powered products, and the gap between shipping an agent and understanding its performance is staggering. Traditional analytics tools weren't designed for systems that reason, make decisions, and execute multi-step workflows autonomously. That's precisely the problem Voker, a YC S24 company, is tackling head-on.

The Observability Crisis in Agentic AI

Let's start with why this matters. When you build a traditional software feature, you instrument it with metrics: page load times, conversion rates, error rates. The cause-and-effect relationships are relatively straightforward. Click button → API call → database query → response. Linear. Predictable.

AI agents obliterate this mental model.

An agent might receive a user request, decompose it into subtasks, call multiple tools, reason about the results, backtrack when hitting dead ends, and synthesize a final response—all while you're watching a loading spinner. The execution path isn't predetermined. It's emergent.

This creates several critical challenges:

Latency becomes multidimensional. It's not just "How long did this take?" but "Why did this take so long? Was it LLM inference? Tool execution? Planning overhead? Unnecessary loops?"

Quality is subjective and contextual. Unlike a 404 error, an agent's response might be technically correct but pragmatically useless. Or vice versa. How do you quantify "helpfulness" at scale?

Failure modes are exotic. Agents don't just crash—they hallucinate, get stuck in reasoning loops, make poor tool choices, or produce outputs that are confidently wrong. Traditional error tracking misses these entirely.

Cost attribution is opaque. When an agent makes 47 LLM calls to answer one question, which calls were necessary? Which were symptoms of poor prompting or tool design?

The teams building serious agentic products are drowning in logs and starving for insights.

What Makes Agent Analytics Different

Voker's emergence signals a maturation in how we think about AI product development. Purpose-built analytics for agents isn't just traditional monitoring with an AI label slapped on—it requires fundamentally different primitives.

Trace-Level Visibility

The atomic unit of agent analytics is the trace: the complete execution graph of a single agent invocation. This includes every LLM call, tool execution, decision point, and intermediate reasoning step.

But raw traces are overwhelming. The insight comes from aggregation and pattern recognition. Which execution paths correlate with user satisfaction? Where do agents consistently waste tokens? What tool combinations predict success?

This is where specialized tooling shines. General-purpose observability platforms force you to build these abstractions yourself. Agent-specific analytics platforms bake them in.

Semantic Evaluation

Here's a hard truth: you can't optimize what you can't measure, and you can't measure agent quality with status codes.

Effective agent analytics requires semantic evaluation—using LLMs to evaluate LLM outputs. This sounds recursive and expensive, but it's the only scalable approach to quality monitoring.

The key is having evaluation frameworks that understand agent-specific failure modes:

These evaluations need to run automatically on production traffic, flagging issues before users complain (or worse, silently churn).

Cost-Performance Tradeoffs

Every agent invocation is a mini P&L statement. You're trading API costs for user value, and the exchange rate varies wildly based on implementation choices.

Agent analytics should surface these tradeoffs explicitly:

I've seen teams reduce agent costs by 60% by identifying and eliminating redundant LLM calls—insights that only became visible with proper instrumentation.

Workflow Understanding

Agents aren't monolithic—they're composed of planning loops, tool execution, memory retrieval, and response generation. Understanding performance requires decomposing these components.

Which part of your agent is the bottleneck? Is it slow tool execution? Verbose prompts causing high latency? Inefficient planning leading to unnecessary steps?

Workflow-aware analytics let you optimize the right things. Caching tool results might be more impactful than switching to a faster LLM. Or vice versa. You need data to know.

Building with Agent Analytics: Practical Applications

Let's move from theory to practice. How should product builders actually use agent analytics?

1. Establish Quality Baselines Early

Don't wait until you have scale to start measuring quality. From day one, instrument your agent with semantic evaluations on a representative test set.

Track these metrics over time:

These baselines are your guardrails. When you ship a prompt change or add a new tool, you need to know if quality regressed before users tell you.

2. Optimize the Expensive Paths First

Not all agent invocations are created equal. Some user intents trigger complex multi-step workflows that consume 10x the tokens of simple queries.

Use analytics to identify your most expensive execution paths, then ask:

I've seen teams achieve dramatic cost reductions by optimizing just the top 5% most expensive traces.

3. Build Feedback Loops into Your Agent

The most sophisticated agent systems use analytics to improve themselves. This isn't full autonomy—it's augmented development.

Examples:

Your analytics platform should make it easy to close these loops, not just surface dashboards.

4. Correlate Agent Behavior with Business Outcomes

Ultimately, agent performance matters only insofar as it drives business results. The holy grail is connecting agent-level metrics to product-level outcomes.

Does higher agent quality correlate with retention? Does latency impact conversion? Do certain tool combinations predict user satisfaction?

This requires integrating agent analytics with your broader product analytics stack. The insights from this correlation are where you find leverage—the agent improvements that actually move the needle on what matters.

The Build vs. Buy Decision

Some teams will be tempted to build agent analytics in-house. After all, it's "just logging and dashboards," right?

Having gone down this path, I'll offer a reality check: building production-grade agent observability is a months-long distraction from your core product.

You need:

Each of these is a non-trivial engineering effort. And unlike your core product, they're undifferentiated infrastructure.

The calculus is simple: if you're building AI agents as your product, invest in your product. Use purpose-built tools for observability.

What to Look for in Agent Analytics Tools

If you're evaluating solutions (whether Voker or alternatives), here are the must-haves:

1. Framework-agnostic instrumentation. You shouldn't be locked into LangChain or any specific agent framework. The tool should work with custom implementations.

2. Automatic evaluation. Manual review doesn't scale. The platform should include or integrate with LLM-based evaluation systems.

3. Cost tracking across providers. Multi-model agents are becoming standard. Your analytics should aggregate costs across OpenAI, Anthropic, and any other APIs you use.

4. Actionable insights, not just data. Dashboards are table stakes. You want anomaly detection, performance recommendations, and quality regressions surfaced proactively.

5. Privacy and security controls. Agent traces contain user data. Ensure the platform supports PII redaction, data retention policies, and compliance requirements.

6. Integration with your workflow. Can you jump from a flagged trace to your logging system? Export data for custom analysis? Trigger alerts in Slack or PagerDuty?

The Future of Agent Development

The emergence of specialized analytics tools for AI agents represents a broader trend: the industrialization of AI product development.

We're moving from the "alchemy phase"—where building agents felt like art—to the "engineering phase," where systematic measurement and optimization become standard practice.

This shift is necessary for AI agents to move from demos to dependable products. Users won't tolerate flaky agents. Businesses won't tolerate unpredictable costs. Developers won't tolerate debugging in the dark.

Voker and tools like it are infrastructure for this transition. They're not sexy—observability never is—but they're essential. The teams that embrace rigorous agent analytics early will ship more reliable products, faster, and with better unit economics.

Getting Started

If you're building AI agents today, here's my recommendation:

Week 1: Instrument your agent with basic tracing. Even if you just log to files, capture the full execution graph.

Week 2: Implement at least one semantic evaluation. Use an LLM-as-judge to score response quality on a scale of 1-10. Track this metric.

Week 3: Identify your three most expensive execution paths. Understand why they're expensive.

Week 4: Evaluate purpose-built analytics tools. Calculate the opportunity cost of building vs. buying.

The teams winning at AI products aren't the ones with the fanciest models—they're the ones with the tightest feedback loops. Agent analytics isn't optional infrastructure. It's competitive advantage.

Welcome to the era of observable AI. The agents that survive in production will be the ones we can actually understand.

Related reading