Testing an AI agent in a demo is easy. The hard part starts after you ship it: once an agent is running unattended in production, it can quietly do the wrong thing for days before anyone notices — burn through your API budget, take an action a human never approved, or just break in a way that's hard to explain to your boss. A new category of tools has grown up specifically for this problem: not testing an agent once before launch, but watching and controlling it every day it's live. This guide covers five real tools built for exactly that, from two different angles — tracing tools that show you what an agent actually did and why, and governance tools that can stop a risky action before it happens.
Watching what your agents actually did: tracing & observability
PandaProbe
PandaProbe is an open-source platform that watches your AI agents in production and can automatically catch — and in some cases fix — regressions before your users notice them. It plugs into LangGraph, CrewAI, and the Claude Agent SDK with one line of instrumentation, and its "Harness" self-healing layer is a genuinely distinct feature: rather than just alerting you, it attempts to correct a broken agent run on its own.
For who: engineering teams running agents in production who need evals, tracing, and automated regression detection in one place.
Price: Hobby tier free; Pro $29/month; Startup $299/month; Enterprise on request. Fully self-hostable under Apache 2.0 if you'd rather not pay at all.
Verdict: the most mature option here (712 GitHub stars, real integrations), but treat the "self-healing" claims as something to verify yourself before letting it touch production unsupervised, and budget for the steep jump from Pro to Startup pricing.
- Strengths: one-line setup, framework integrations, real self-hosting option, active community
- Limits: Pro→Startup price jump is steep; enterprise pricing isn't public; self-healing needs independent verification
Tracea
Tracea is a free, self-hosted dashboard that shows you exactly what your AI agents did, why they cost what they cost, and where they broke. It uses transport-level tracing, meaning there's no SDK to wrap your code in, and it can run root-cause analysis locally through Ollama — fully offline, with zero data leaving your infrastructure.
For who: developers and small teams who want on-prem observability with no vendor lock-in and no per-event bill.
Price: completely free, MIT-licensed, self-hosted. No paid tier exists.
Verdict: the most privacy-friendly and cheapest option by far, but it's a very early project (7 GitHub stars at time of writing, no listed company behind it) — you're hosting and maintaining it yourself with no managed fallback if something goes wrong.
- Strengths: $0 forever, no vendor lock-in, offline-capable, YAML-configurable Slack/webhook alerts
- Limits: very early stage, no managed cloud option, smaller community than funded competitors
Prefactor
Prefactor is an observability and reliability layer that scores every single agent run in real time — and, unlike a pure logging tool, can actually pause a risky action before it happens rather than just recording it after the fact. It ships framework-agnostic SDKs covering LangChain, Claude, Vercel AI, and more.
For who: teams whose agents are already handling real stakes in production, and compliance/security teams in regulated industries who need an auditable approval workflow.
Price: free up to 25,000 spans/month; Scaleup tier $250/month (100k spans, then $2.50 per additional 1,000) or $9,600/year; Enterprise from 4M+ spans/month, custom pricing.
Verdict: the only tool in this list that intervenes in real time rather than just reporting afterward, which makes it the strongest pick once an agent has real financial or safety stakes — but it only pays off once you already have production traffic to protect, and costs can climb fast past the free tier.
- Strengths: real-time pause/block before risky actions, generous free tier, framework-agnostic, built for regulated industries
- Limits: most valuable once you already have production stakes; requires defining your own evaluation policies; cost scales with trace volume
Controlling what your agents are allowed to do: governance & guardrails
Lunen
Lunen lets an entire company build and run AI agents while giving the security team a way to actually see and control what those agents are allowed to do — per-tool policies (auto-execute vs. require human approval), plus a unified audit log of what every agent accessed. It's built by REDspace, an engineering firm with 25+ years behind it, not a brand-new startup.
For who: large organizations with a security/compliance review process that want to roll out AI agents internally without losing oversight.
Price: Team plan in early access (50,000 tool calls/month included, pricing not yet public); Enterprise custom, with dedicated or BYOC (bring-your-own-cloud) deployment available.
Verdict: a serious enterprise play with real per-tool policy controls, but it's still in a design-partner phase — expect to talk to sales, not sign up self-serve, and there's no separate funding information for Lunen distinct from its parent company.
- Strengths: per-tool execution policies, unified audit trail, BYOC deployment, backed by an established engineering firm
- Limits: not generally available yet, Team pricing not public, enterprise-first positioning may be overkill for a small team
Lyzr
Lyzr is infrastructure for large companies to put AI agents into actual production safely — with a 7-layer governance stack covering anti-hallucination guardrails, PII protection, and audit logging — instead of stopping at an impressive-looking prototype that never ships. It works on top of whatever framework or LLM you're already using, rather than forcing you onto a new one, and comes with 200+ pre-built, production-tested agents.
For who: large enterprises in regulated sectors (banking, healthcare, insurance, HR, fintech) that need to deploy agents with governance and compliance baked in.
Price: usage-based — $0.08 per agent execution on managed cloud, or $0.03 per execution for VPC/on-premise deployment. LLM and compute costs billed separately. No free tier.
Verdict: built for companies that already know they're deploying at scale, with 500+ enterprise customers as social proof — but the lack of any free tier and the per-execution pricing model make it a poor fit for a team still prototyping its first agent.
- Strengths: works over existing frameworks/LLMs, deep 7-layer governance stack, large library of pre-built agents, proven at enterprise scale
- Limits: no free tier, docs don't exhaustively list every supported framework, not built for early prototyping
Side-by-side comparison
| Tool | Angle | Starting price | Best for |
|---|---|---|---|
| PandaProbe | Tracing + auto-fix | Free (self-hosted) / $29 mo | Teams wanting evals + regression detection out of the box |
| Tracea | Tracing | $0 forever (self-hosted, MIT) | Privacy-first teams who don't want a vendor bill at all |
| Prefactor | Real-time intervention | Free up to 25k spans/mo | Agents with real financial or safety stakes |
| Lunen | Governance + audit | Early access, custom | Security teams rolling out agents company-wide |
| Lyzr | Governance + compliance | $0.08/agent run, no free tier | Regulated enterprises deploying at scale |
None of these tools do the same job as a pre-launch eval suite — they're for after you've shipped, when the real question stops being "does this agent work in theory" and becomes "what is it actually doing right now, and can I stop it if it's wrong." If you're a small team that just wants visibility without paying anything, start with Tracea or the free tier of PandaProbe. If your agents already touch money or sensitive data, Prefactor's real-time pause is worth the price. And if you're rolling agents out across an entire regulated organization, Lunen or Lyzr are built for exactly that scale of oversight.