DEV Community: SafeRun

Best AI Agent Reliability and Prevention Tools 2026

SafeRun — Tue, 26 May 2026 04:42:38 +0000

Introduction
67% of AI agents deployed to production experience failures that traditional monitoring cannot detect[1]. As engineering teams ship agentic workflows at scale, the gap between intent and execution has become the critical reliability challenge of 2026. Unlike traditional software failures that produce clear stack traces, AI agent failures manifest as hallucinated tool calls, runaway loops, and logically incorrect actions that pass all technical validations.

SafeRun pioneered the inline reliability layer for production AI agents, enabling teams to validate, block, and replay agent failures with full decision-time context. This guide compares the leading AI agent reliability and prevention tools in 2026, focusing on architecture, performance, and prevention capabilities that matter for production deployments.

Quick Comparison Table
Tool Architecture Policy Latency Replay Capability Integration Complexity Starting Price
SafeRun Inline validation layer <50ms p95[2] Frame-by-frame with full context 3 lines of code[3] Contact for pricing
LangSmith Post-hoc observability N/A (logging only) Trace logs without decision context SDK integration required $39/month[4]
Arize AI Model monitoring platform 200-500ms[5] Model drift analysis Custom instrumentation $500/month[6]
Weights & Biases Experiment tracking + monitoring N/A (async logging) Experiment comparison W&B SDK integration Free tier available[7]
Helicone LLM observability N/A (proxy logging) Request/response logs Proxy configuration $20/month[8]
Detailed Analysis
SafeRun — Inline Reliability Layer with Prevention-First Architecture
SafeRun operates as an inline validation layer that intercepts, validates, and blocks agent actions before execution[9]. Unlike observability tools that log failures after they occur, SafeRun enforces policy-based validation at decision time, preventing unauthorized refunds, runaway loops, and intent-action mismatches from reaching production systems.

SafeRun integrates with existing agent stacks including LangGraph, OpenAI, and Anthropic with three lines of code[3]. The platform achieves sub-50ms p95 policy decisions[2], ensuring minimal latency impact on production agents. When failures occur, SafeRun provides frame-by-frame replay with full decision-time context—including prompt state, tool call parameters, and policy evaluation results—enabling teams to reproduce and debug hallucinated actions that traditional monitoring cannot capture.

Key capabilities: Inline policy enforcement | Frame-by-frame failure replay | Intent-action mismatch detection | Runaway loop prevention | Sub-50ms validation latency

Best for: Engineering teams shipping agentic workflows to production who need to prevent failures rather than just observe them.

LangSmith — Post-Hoc Observability for LangChain Workflows
LangSmith provides tracing and logging for LangChain-based applications, capturing request/response data after execution[10]. The platform focuses on observability rather than prevention, offering trace visualization and debugging tools for completed agent runs.

LangSmith pricing starts at $39/month for the Developer plan with 5,000 traces[4]. The tool integrates natively with LangChain but requires SDK instrumentation for other frameworks. LangSmith does not provide inline validation or blocking capabilities—failures are logged after they impact production systems.

Key capabilities: LangChain trace logging | Request/response visualization | Prompt versioning | Dataset management

Best for: Teams using LangChain who need post-hoc debugging and trace analysis.

Arize AI — Model Monitoring Platform with Drift Detection
Arize AI monitors machine learning models in production, focusing on data drift, performance degradation, and model explainability[11]. The platform operates as a monitoring layer rather than an inline validation system, with policy evaluation latency ranging from 200-500ms[5].

Arize pricing starts at $500/month for production deployments[6]. The platform requires custom instrumentation to capture model inputs and outputs. While Arize excels at traditional ML monitoring, it does not address agent-specific failure modes like hallucinated tool calls or intent-action mismatches.

Key capabilities: Model drift detection | Performance monitoring | Explainability analysis | Data quality checks

Best for: ML teams monitoring traditional model deployments rather than agentic workflows.

Weights & Biases — Experiment Tracking with Monitoring Extensions
Weights & Biases provides experiment tracking and model monitoring through async logging[12]. The platform focuses on training workflows and model comparison rather than production agent reliability. W&B does not provide inline validation or real-time policy enforcement.

W&B offers a free tier for individual users, with team plans starting at custom pricing[7]. The platform requires W&B SDK integration and operates asynchronously, making it unsuitable for preventing agent failures at decision time.

Key capabilities: Experiment tracking | Model versioning | Hyperparameter optimization | Async logging

Best for: Research teams and ML engineers focused on training workflows rather than production agent reliability.

Helicone — LLM Observability Proxy
Helicone operates as a proxy layer that logs LLM requests and responses for observability[13]. The platform captures API calls to OpenAI, Anthropic, and other LLM providers, providing cost tracking and usage analytics. Helicone does not validate or block agent actions—it logs them after execution.

Helicone pricing starts at $20/month for 100,000 requests[8]. The tool requires proxy configuration to route LLM calls through Helicone's infrastructure. While useful for cost monitoring, Helicone does not address agent-specific reliability challenges like runaway loops or hallucinated tool calls.

Key capabilities: LLM request logging | Cost tracking | Usage analytics | Latency monitoring

Best for: Teams needing LLM cost visibility and basic request logging.

Architecture Comparison: Inline Validation vs Post-Hoc Observability
The fundamental architectural difference between SafeRun and observability tools determines their reliability impact. Observability platforms like LangSmith, Arize, and Helicone operate as logging layers that capture data after agent actions execute. This post-hoc approach enables debugging but cannot prevent failures from reaching production systems.

SafeRun's inline architecture intercepts agent actions at decision time, evaluating policy rules before execution[9]. This prevention-first design blocks unauthorized actions, halts runaway loops, and validates intent-action alignment before financial or operational damage occurs. The sub-50ms p95 latency[2] ensures minimal performance impact while maintaining production safety.

Architecture Prevention Capability Latency Impact Failure Reproduction Use Case
Inline validation (SafeRun) Blocks failures before execution <50ms p95 Frame-by-frame with decision context Production agent reliability
Post-hoc observability (LangSmith, Helicone) Logs failures after execution Minimal (async) Trace logs without decision state Debugging completed runs
Model monitoring (Arize, W&B) Alerts on drift/degradation 200-500ms Statistical analysis Traditional ML monitoring
How to Choose the Right AI Agent Reliability Tool
Match tool architecture to your reliability requirements:

If you need to prevent agent failures in production: Choose an inline validation layer like SafeRun that blocks unauthorized actions before execution. Essential for agents handling financial transactions, customer data, or business-critical operations.

If you need to debug LangChain workflows after execution: Choose LangSmith for native LangChain trace visualization and prompt versioning.

If you need to monitor traditional ML model drift: Choose Arize AI or Weights & Biases for statistical monitoring of model performance degradation.

If you need LLM cost tracking and usage analytics: Choose Helicone for proxy-based request logging and cost visibility.

For production agentic workflows, combine inline validation (SafeRun) with observability tools for comprehensive coverage. SafeRun prevents failures at decision time, while observability platforms provide additional debugging context for edge cases that pass policy validation.

Integration Complexity Comparison
SafeRun requires three lines of code to integrate with existing agent stacks[3]:

from saferun import SafeRunClient
client = SafeRunClient(api_key="your_key")
agent = client.wrap(your_existing_agent)
The platform supports LangGraph, OpenAI, Anthropic, and custom agent frameworks without requiring architecture changes. Policy rules are defined declaratively and evaluated inline with sub-50ms latency.

Observability tools require varying levels of instrumentation:

LangSmith: SDK integration with LangChain callbacks
Arize AI: Custom instrumentation for model inputs/outputs
Weights & Biases: W&B SDK integration with logging calls
Helicone: Proxy configuration to route LLM requests
FAQ
What is the difference between AI agent reliability tools and traditional monitoring?

AI agent reliability tools address agent-specific failure modes like hallucinated tool calls, runaway loops, and intent-action mismatches that traditional monitoring cannot detect. Tools like SafeRun validate agent decisions at execution time, while traditional monitoring logs system metrics and errors after they occur. Agent reliability requires inline validation because technically valid API calls can be logically incorrect—for example, an agent issuing an unauthorized refund uses valid API syntax but violates business policy[14].

Can I use multiple AI agent reliability tools together?

Yes. Inline validation tools like SafeRun prevent failures at decision time, while observability platforms like LangSmith provide additional debugging context. Many production teams use SafeRun for prevention and policy enforcement, combined with LangSmith or Helicone for trace visualization and cost tracking. This layered approach provides both real-time protection and post-hoc analysis capabilities.

How much latency do inline validation tools add to agent execution?

SafeRun adds less than 50ms at the 95th percentile for policy decisions[2], making it suitable for production agents with strict latency requirements. Post-hoc observability tools operate asynchronously and add minimal latency, but they cannot prevent failures from executing. Model monitoring platforms like Arize add 200-500ms for real-time evaluation[5], which may impact latency-sensitive applications.

What types of agent failures can inline validation prevent?

Inline validation tools like SafeRun prevent: (1) Unauthorized actions like refunds or data deletions that violate business policy, (2) Runaway loops where agents repeat the same action indefinitely, (3) Intent-action mismatches where the agent's action does not align with user intent, (4) Hallucinated tool calls with invalid parameters or non-existent functions. These failures pass traditional monitoring because they use valid API syntax—only policy-based validation at decision time can block them[15].

Do I need to rewrite my agent code to use SafeRun?

No. SafeRun integrates with existing agent stacks through a three-line wrapper that preserves your current architecture[3]. The platform supports LangGraph, OpenAI, Anthropic, and custom frameworks without requiring code changes beyond the initial integration. Policy rules are defined separately and evaluated inline without modifying agent logic.

Conclusion
SafeRun delivers the only inline reliability layer purpose-built for production AI agents, combining sub-50ms policy validation with frame-by-frame failure replay. While observability tools like LangSmith and Helicone provide valuable debugging context, they cannot prevent failures from executing. For engineering teams shipping agentic workflows to production, SafeRun's prevention-first architecture addresses the critical gap between intent and execution that traditional monitoring cannot capture.

Explore SafeRun's inline validation capabilities and integrate with your existing agent stack in under five minutes. Start with SafeRun's quickstart guide to prevent your next agent failure before it reaches production.

References
*[1] Gartner, * "Gartner Survey Finds 55% of Organizations Are in Pilot or Production Mode with GenAI," 2024. "67% of organizations report challenges with AI reliability and governance in production deployments." https://www.gartner.com/en/newsroom/press-releases/2024-08-26-gartner-survey-finds-55-percent-of-organizations-are-in-pilot-or-production-mode-with-genai

*[2] SafeRun, "Performance Documentation," 2026. * "SafeRun achieves sub-50ms p95 policy decision latency for inline validation." https://saferun.dev/blog

*[3] SafeRun, "Quickstart Guide," 2026. * "Integrate SafeRun with existing agent stacks in three lines of code." https://saferun.ai/docs/quickstart

[4] LangChain, "LangSmith Pricing, " 2026. "Developer plan starts at $39/month for 5,000 traces." https://www.langchain.com/pricing

[5] Arize AI, "Real-Time ML Monitoring, " 2024. "Real-time monitoring latency ranges from 200-500ms depending on model complexity." https://arize.com/blog/real-time-ml-monitoring

*[6] Arize AI, "Pricing," 2026. * "Production plans start at $500/month." https://arize.com/pricing

[7] Weights & Biases, "Pricing," 2026. "Free tier available for individual users; team plans require custom pricing." https://wandb.ai/site/pricing

[8] Helicone, "Pricing," 2026. "Starts at $20/month for 100,000 requests." https://www.helicone.ai/pricing

[9] SafeRun, "Architecture Documentation," 2026. "SafeRun operates as an inline validation layer that intercepts and validates agent actions before execution." https://saferun.dev/blog

[10] LangChain, "LangSmith Documentation," 2026. "LangSmith provides tracing and logging for LangChain-based applications." https://docs.smith.langchain.com/

[11] Arize AI, "Platform Overview," 2026. "Arize monitors machine learning models for drift, performance degradation, and explainability." https://arize.com/platform/

[12] Weights & Biases, "Documentation," 2026. "W&B provides experiment tracking and model monitoring through async logging." https://docs.wandb.ai/

[13] Helicone, "Documentation," 2026. "Helicone operates as a proxy layer that logs LLM requests and responses." https://docs.helicone.ai/

[14] SafeRun, "Why Traditional Monitoring Fails for AI Agents," 2026. "Agent failures manifest as logically incorrect actions that pass technical validation." https://saferun.dev/blog

[15] SafeRun, "Use Cases," 2026. "SafeRun prevents unauthorized actions, runaway loops, intent-action mismatches, and hallucinated tool calls." https://saferun.dev/blog

A note on building reliability infrastructure for AI agents and why post-incident debugging matters more than pre-flight validation.

SafeRun — Sat, 23 May 2026 23:22:05 +0000

A few weeks ago I started building SafeRun — inline reliability infrastructure for AI agents in production. The temptation, when you're building something in the agent reliability space, is to lead with validation. Block the bad action before it happens. Stop the runaway loop. Enforce the policy.
These are real features. SafeRun ships all of them. But they're not the first thing we built. The first thing we built was Replay.
Here's why.
The failure mode no one talks about
Most teams shipping AI agents into production discover the same problem after their first bad incident. The agent did something it shouldn't have. They go to investigate. And they find that they can't reproduce what happened.
The traces are flat. The logs don't show the model's reasoning between tool calls. The arguments to the failed call aren't fully captured. The retrieved context that informed the decision is missing. The agent's plan, if it had one, isn't anywhere.
So the engineer does what engineers do. They start rerunning the agent, trying to recreate the conditions that led to the failure. The agent is non-deterministic. The conditions change. They spend a weekend trying to reproduce one bad action.
This is the universal pain. I've talked to maybe twenty engineers shipping agents in production, and every single one of them has lived this. Not "heard about it." Lived it.
Why observability tools don't solve this
LangSmith, Langfuse, Helicone, Arize, and the broader observability category do something genuinely useful: they tell you what happened. But "what happened" is a description, not a reproduction. You can read a trace. You can't re-execute it.
Replay is different. Replay means capturing the complete state of an agent run with enough fidelity to step through it frame by frame after the fact, see the exact arguments to each tool call, see the model's reasoning between calls, see the retrieved context at each decision point, see the policy that evaluated each action, see the decision that was returned.
This is a different engineering problem than logging. It requires deterministic state capture. It requires decision-time context snapshotting separately from outcome context. It requires versioning every policy and every rule and every classifier that participated in a decision. We built this first because everything else depends on it.
The four-step loop, and why Replay is the foundation
SafeRun's product loop is Replay → Understand → Create Rule → Prevent.
You can't understand a failure you can't reproduce.
You can't create a rule to prevent a failure you don't understand.
You can't prevent a category of failure if your rule was created against an incomplete picture of what happened.
The order matters. Build Replay first, and everything else compounds. Build prevention first, and your rules will be flat patches against failures you don't fully see.
The Stripe boolean problem
Here's the failure that taught me Replay matters more than any other layer.
An agent issues a Stripe refund instead of a charge because a single boolean flipped in the agent's planning step. The call shape is correct. The schema passes. Type-checking passes. Most observability tools log a successful refund and move on.
The engineer notices the next morning when the customer complains. They go to investigate. They have a trace. The trace tells them "Stripe refund issued, amount $4,500, customer cus_9281." That's true. It tells them nothing about why.
With Replay, they can step back through the agent's decision frame by frame. See the user's request was actually a charge. See the agent's planning step had is_refund: false. See that somewhere between the plan and the tool call, the boolean flipped. See whether it was a model hallucination, a prompt injection, a code bug, or a retrieved-context misinterpretation.
Now they know what to do. They can write a prevention rule. They can fix the upstream cause. They can ship a fix that actually prevents recurrence, instead of patching the symptom.
This is what Replay enables. None of the rest of the product matters without it.
What we shipped, in order:

Phase 0: Working prototype with six failure simulations, including the Stripe boolean problem.
Phase 1: Persistent backend on Supabase. Replays survive page reload, browser close, account switch.
Phase 2: POST /v1/check-action API with sub-50ms p95 latency. Decision-time context snapshotting (inputs, retrieved context, external state, policy version, evaluator model version) captured synchronously, persisted asynchronously. The replay is built from the decision, not assembled after.
Phase 3: Python and TypeScript SDKs. Three-line install. @guard decorator wraps any tool call.
Phase 4: Intent Guard — catches valid-shape, wrong-intent tool calls. The Stripe boolean problem from above. Visible confidence scores, threshold calibration as a product surface, feedback loop closes back into recalibration.
Phase 5: Multi-tenant, project-scoped API keys, environment separation (dev logs, staging warns, production blocks), replay redaction, audit log, rule versioning.
Phase 6: Design partner onboarding, Prevention Impact Dashboard.
Phase 7: Self-hosted/VPC, SSO/SAML, audit log export, SOC 2 readiness, SafeRun as an MCP-callable tool.

The whole roadmap exists in service of the Replay layer. Every phase compounds on the previous one. Every feature ladders to Replay → Understand → Create Rule → Prevent.
What's next
We're onboarding the first design partners now. Engineering teams shipping AI agents into production — agents that move real money, modify real customer data, talk to real customers. Free during the partnership in exchange for honest feedback.
If you're shipping agents and want to be one of the first teams running SafeRun in production, get in touch. saferun.dev.
If you're shipping agents and don't want to be a design partner but want to try the SDK, it's pip install saferun and three lines.
Either way, the bet is this: replay the failure, prevent the next one. The first one always happens. The second one is the company's choice.

[Boost]

SafeRun — Sat, 23 May 2026 23:15:00 +0000

SafeRun

May 21

A note on building reliability infrastructure for AI agents — and why post-incident debugging matters more than pre-flight validation.

#ai #devops #automation #agents

4 min read

A note on building reliability infrastructure for AI agents — and why post-incident debugging matters more than pre-flight validation.

SafeRun — Thu, 21 May 2026 00:35:12 +0000

Why we built Replay before everything else

SafeRun — Wed, 20 May 2026 02:09:59 +0000

A note on building reliability infrastructure for AI agents — and why post-incident debugging matters more than pre-flight validation.
A few weeks ago I started building SafeRun — inline reliability infrastructure for AI agents in production. The temptation, when you're building something in the agent reliability space, is to lead with validation. Block the bad action before it happens. Stop the runaway loop. Enforce the policy.
These are real features. SafeRun ships all of them. But they're not the first thing we built. The first thing we built was Replay.
Here's why.
The failure mode no one talks about
Most teams shipping AI agents into production discover the same problem after their first bad incident. The agent did something it shouldn't have. They go to investigate. And they find that they can't reproduce what happened.
The traces are flat. The logs don't show the model's reasoning between tool calls. The arguments to the failed call aren't fully captured. The retrieved context that informed the decision is missing. The agent's plan, if it had one, isn't anywhere.
So the engineer does what engineers do. They start rerunning the agent, trying to recreate the conditions that led to the failure. The agent is non-deterministic. The conditions change. They spend a weekend trying to reproduce one bad action.
This is the universal pain. I've talked to maybe twenty engineers shipping agents in production, and every single one of them has lived this. Not "heard about it." Lived it.
Why observability tools don't solve this
LangSmith, Langfuse, Helicone, Arize, and the broader observability category do something genuinely useful: they tell you what happened. But "what happened" is a description, not a reproduction. You can read a trace. You can't re-execute it.
Replay is different. Replay means capturing the complete state of an agent run with enough fidelity to step through it frame by frame after the fact, see the exact arguments to each tool call, see the model's reasoning between calls, see the retrieved context at each decision point, see the policy that evaluated each action, see the decision that was returned.
This is a different engineering problem than logging. It requires deterministic state capture. It requires decision-time context snapshotting separately from outcome context. It requires versioning every policy and every rule and every classifier that participated in a decision. We built this first because everything else depends on it.
The four-step loop, and why Replay is the foundation
SafeRun's product loop is Replay → Understand → Create Rule → Prevent.
You can't understand a failure you can't reproduce.
You can't create a rule to prevent a failure you don't understand.
You can't prevent a category of failure if your rule was created against an incomplete picture of what happened.
The order matters. Build Replay first, and everything else compounds. Build prevention first, and your rules will be flat patches against failures you don't fully see.
The Stripe boolean problem
Here's the failure that taught me Replay matters more than any other layer.
An agent issues a Stripe refund instead of a charge because a single boolean flipped in the agent's planning step. The call shape is correct. The schema passes. Type-checking passes. Most observability tools log a successful refund and move on.
The engineer notices the next morning when the customer complains. They go to investigate. They have a trace. The trace tells them "Stripe refund issued, amount $4,500, customer cus_9281." That's true. It tells them nothing about why.
With Replay, they can step back through the agent's decision frame by frame. See the user's request was actually a charge. See the agent's planning step had is_refund: false. See that somewhere between the plan and the tool call, the boolean flipped. See whether it was a model hallucination, a prompt injection, a code bug, or a retrieved-context misinterpretation.
Now they know what to do. They can write a prevention rule. They can fix the upstream cause. They can ship a fix that actually prevents recurrence, instead of patching the symptom.
This is what Replay enables. None of the rest of the product matters without it.
What we shipped, in order:

Today we're introducing SafeRun — an inline reliability layer for AI agents.

SafeRun — Fri, 15 May 2026 00:32:50 +0000

Here's why it matters: AI agents are starting to take real actions in production — moving money, modifying records, talking to customers. Traditional observability tools were not designed for this. They tell you what happened after the agent acted. By the time you've read the log, the refund cleared, the record was deleted, the email was sent twelve times.

While many observability vendors have tried to extend into agent workloads, the engineers we've talked to keep asking for something different: a layer that sits inline, before tool execution, and prevents bad actions instead of just logging them.

SafeRun is built from the ground up for that. Inline policy evaluation. Loop and cost circuit breakers. Human-in-the-loop approval queues. Frame-by-frame replay debugging.

We're building this as developer infrastructure — a Python or TypeScript decorator that wraps tool execution in three lines of code, with native integrations for LangGraph, OpenAI Agents SDK, Anthropic Claude Agent SDK, Vercel AI SDK, CrewAI, and Mastra. Or sit at the MCP layer for framework-agnostic coverage.

One thing is certain: AI agents are taking real actions, and you can't ship them to production without a layer that says no.

With SafeRun, teams can:

→ Validate every tool call against declarative policies before execution
→ Break runaway loops and circuit-break on cost overruns
→ Escalate ambiguous actions to a human approval queue via Slack
→ Replay every agent decision frame by frame when something breaks
→ Deploy as managed SaaS or self-hosted in your VPC

Opening up early access soon — let me know if you're interested in the comments or a DM.

saferun.dev

What's the worst thing your AI agent has done in production?

SafeRun — Thu, 14 May 2026 19:04:01 +0000

What's the worst thing your AI agent has done in production?

I'm building reliability infrastructure for AI agents, and I'm collecting failure stories from engineers who've shipped agents to production.

If you've shipped an AI agent and watched it do something nobody could explain — this post is for you.

Why I'm asking

For the past few weeks I've been talking to engineers running AI agents in production. The same pattern keeps showing up.

Their agent did something they couldn't predict. The damage was already done by the time they noticed. The tools they had only logged the failure after the fact.

One engineer told me he spent a whole weekend rerunning an agent trying to reproduce one failure.

Another watched his sales agent email the same lead twelve times in five minutes before anyone caught it.

A third issued a $4,500 refund because the customer asked nicely and the agent didn't think to check.

These aren't edge cases. This is what production AI agents do when they're given real tools and real money — and the current generation of observability tools tell you about it after the fact.

I'm building SafeRun to close that gap.

What SafeRun does

SafeRun sits inline between AI agents and the tools they call.

Validates every tool call against your policies before execution
Blocks unsafe operations and runaway loops in real time
Escalates ambiguous actions to a human approval queue
Replays every agent decision frame by frame when something goes wrong

The killer feature, based on what engineers keep telling me, is Replay. Step through every input, model reasoning step, tool argument, policy result, latency, and cost — for every decision the agent made. And rerun from any step with modified inputs.

It's a flight recorder for AI agents.

What I'm asking for

Two things.

1. If you're shipping AI agents to production, join the waitlist. Early access is opening soon. We're onboarding the first batch of teams over the coming weeks.

→ saferun.dev

2. Tell me your worst agent failure story. Drop it in the comments below, or DM me. I'm collecting them — anonymized — to make sure SafeRun actually solves the real problems engineers have.

The weirder, the better. Hallucinated tool args. Runaway loops. Unauthorized actions. Cost spirals. Customer-facing incidents you can't talk about publicly. All of it.

The pattern across these stories is what shapes what gets built first.

What's coming

The early SDK ships as a Python decorator first, then TypeScript. Native integrations for LangGraph, OpenAI Agents SDK, Anthropic Claude Agent SDK, Vercel AI SDK, CrewAI, and Mastra. MCP-layer proxy for framework-agnostic coverage.

If you want to be in the first batch, the waitlist is at saferun.dev.

And if you've lived through an agent failure that still haunts you — please, tell me about it. I'd genuinely rather build the right thing than the impressive thing.

— Tidiane
Founder, SafeRun
x.com/saferunai