RunLog AI
ProductPricingDocsBlog
SOC2-ready
SDKs: Python/TS

Trust, Observability & Runtime Control for AI Agents

RunLog AI records every agent step, enforces policies before risky actions, and replays runs to fix issues—before users are impacted.

View Docs

See every step. Stop unsafe actions. Replay to fix.

Simple Integration

Add RunLog AI to your existing agents with just a few lines of code.

from runlog import RL

rl = RL(service="support", env="prod")

with rl.run(task="Refund request #123"):
    docs = rl.tool("kb.search", query="refund policy")
    rl.enforce("retrieval_confidence", min=0.8, evidence=docs)
    
    answer = llm(prompt=compose(docs))
    
    if "refund" in answer.lower():
        rl.enforce("require_supervisor_for_refund", amount=250)
        rl.tool("payments.refund", amount=250, customer_id="c_42")

Frequently Asked Questions

Everything you need to know about RunLog AI.

RunLog AI

RunLog AI records every agent step, enforces policies before risky actions, and replays runs to fix issues—before users are impacted.

Product

  • Features
  • Pricing
  • Documentation
  • API Reference

Company

  • About
  • Blog
  • Careers
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  • Security
  • Status

© 2025 RunLog AI. All rights reserved.

Built with Next.js and Tailwind CSS

Three Pillars of Agent Safety

Complete visibility, proactive control, and iterative improvement for your AI agents.

Flight Recorder
Step-by-step traces of prompts, tools, inputs/outputs, cost, and latency.
Policy Firewall
Deny, modify, or escalate risky calls (loops, PII, budgets) at runtime.
Replay Lab
Deterministic replays and what-ifs to validate prompts, models, and policies.

See Every Step in Action

Real-time monitoring and control for production AI agents.

Real-time Execution Tracking

Watch your agents execute step-by-step with cost and latency monitoring.

Agent Execution Timeline

Total: $0.000
Total: 0ms
1

Query Processing

Parse user input and extract intent

2

Knowledge Retrieval

Search knowledge base for relevant documents

3

Policy Check

Validate against security policies

4

LLM Generation

Generate response using GPT-4

5

Response Validation

Verify output quality and safety

Policy Configuration

Define safety policies with simple YAML configuration and real-time validation.

Policy Configuration
Valid
version: 1
policies:
  - id: refund_supervisor
    when: 
      tool: "payments.refund"
      args.amount: { gt: 100 }
      not: { tags: ["supervisor_approved"] }
    action: deny
    
  - id: pii_protection
    when: 
      tool: ["identity.get_ssn", "identity.get_card"]
      not: { tags: ["compliance_approved"] }
    action: deny

groups:
  - id: prod_safety
    includes: ["refund_supervisor", "pii_protection"]

assignments:
  - target: 
      service: "support"
      env: "prod"
    use_groups: ["prod_safety"]
    mode: dry

Policy Performance Analysis

Compare policy effectiveness with detailed metrics and cost analysis.

Policy Performance Comparison

Baseline

Actions Blocked
12
Total Cost
$45.67
Issues Found
8

Candidate

Actions Blocked
28
+16
Total Cost
$32.14
-$13.53
Issues Found
2
-6

What Teams Are Saying

See how RunLog AI is helping teams build safer, more reliable AI agents.

"RunLog AI gave us the confidence to deploy our customer service agents to production. The policy enforcement caught issues we never would have seen coming."
SC
Sarah Chen
VP of Engineering, TechFlow
"The replay functionality is a game-changer. We can test policy changes against historical runs before deploying, which has saved us countless hours of debugging."
MR
Marcus Rodriguez
AI Safety Lead, DataSync
"Finally, observability for AI agents that actually works. The step-by-step traces make it easy to understand what our agents are doing and optimize their performance."
EW
Emily Watson
CTO, ScaleUp