AI Agent Monitoring & Optimization

Debug, Optimize, and Control Your AI Agents

See exactly what your AI agents are doing, why they make decisions, and where they're burning tokens. Track performance, debug failures, and optimize costs - all from one dashboard.

AgentOps Platform

Complete Observability & Control

Build production-ready AI agents with tools that actually help you ship. No more guessing why agents fail or wondering where your money went.

📱

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Pillar 1

Complete Observability

Watch your agents think in real-time. See every decision they make, trace through their reasoning chains, and spot issues before they cost you money. When something breaks, you'll know exactly what went wrong and how to fix it.

-18%
247ms
P95 Latency
+3.1%
87.3%
Prompt Cache Hit Rate
+2.4%
91.8%
Tool Call Success Rate
+0.3%
99.2%
Agent Uptime

Performance Monitoring

Memory Usage2.4 GB
CPU Usage42%
Network I/O850 Mb/s

Decision Tracing

Input Processing23ms
Context Retrieval87ms
Reasoning Chain243ms
Output Generation134ms

Real-time Agent States

Active
37
Idle
12
Processing
18
Error
5

Real-time Alerts

High latency detected in Agent-0012m ago
Token usage spike in processing cluster5m ago
Memory threshold reached in Agent-00312m ago
Network connectivity issues resolved15m ago

Widget-Based Dashboards

Drag-and-drop widgets to build your own monitoring view. Save layouts per team. We keep breaking the grid system accidentally (working on it).

7-Day Trace Retention

Compare traces from last week to debug regressions. Queries get slow after day 5 (we're optimizing the indexes).

Mobile Alerts (Beta)

Push notifications for critical errors. iOS only right now. Android version needs better battery optimization first.

Pillar 2

Agent Lifecycle Management

Manage agents from prototype to production. Test changes safely, roll out updates without breaking things, and roll back when something goes wrong. Version control for AI agents that actually works.

In Progress
Development
3 Agents
QA Phase
Testing
5 Agents
Stable
Production
12 Agents
Updating
Maintenance
2 Agents

Development Pipeline

Version ControlActive Branch
feature/smart-routingUpdated 2h ago
15 commits ahead
Testing Progress78% Coverage
Unit Tests132/156 passing
Integration Tests34/48 passing

Deployment Pipeline

Development
Active
Continuous
Staging
Ready
2h ago
Production
Stable
1d ago

Version Management

Customer Service Agentv2.4.1
Current
Data Processing Agentv1.8.5
Update Available
Analytics Agentv3.0.0
Beta Testing

Performance Evolution

Accuracy Improvement+8.7%
Previous: 78.1%Current: 86.8%
Response Time-73ms
Previous: 312msCurrent: 239ms

Canary Deployments

Route 5% of traffic to new versions first. Auto-rollback if error rates spike. Still tuning the thresholds (sometimes rolls back too aggressively).

Prompt Injection Detection

Runs regex patterns and ML classifiers on prompts before deployment. Catches ~89% of known attacks. False positives happen with creative writing agents.

A/B Version Testing

Run two versions simultaneously, compare outcomes. Statistical significance calculator included. Sample size recommendations sometimes too conservative.

Pillar 3

Advanced Orchestration

Run multiple agents that actually work together. Route tasks to the right agent, balance loads automatically, and watch them collaborate without stepping on each other. Less manual coordination, more getting stuff done.

+3
24
Active DAGs
+12%
1.2K/hr
Message Throughput
+1.2%
94.7%
Task Completion Rate
-0.4s
2.3s
Median Exec Time

Active Workflows

Customer Support Pipeline
5 Agentssequential
78%
Load
Data Processing Cluster
8 Agentsparallel
92%
Load
Research Analysis Network
4 Agentsmesh
45%
Load

Communication Patterns

Direct+12%
845 messages/hrAvg. Latency: 67ms
Broadcast-5%
234 messages/hrAvg. Latency: 124ms
Chain+8%
567 messages/hrAvg. Latency: 189ms

Resource Allocation

Compute Unitsoptimal
75 / 100 units75% utilized
Memory Poolwarning
12.8 / 16 units80% utilized
Network Bandwidthoptimal
8.5 / 10 units85% utilized

Task Distribution

NLP Tasks
450
Data Processing
325
Analysis
275
Decision Making
180
Auto-scaling is enabled. The system will optimize resource allocation based on task distribution patterns.

Least-Loaded Routing

Routes requests to least busy agents using token count + queue depth heuristic. Doesn't account for model speed differences yet (on roadmap).

YAML Workflow Builder

Define DAGs in YAML with conditional branching. Visual editor in beta (still buggy with complex loops). Most users just write YAML.

Webhook Integrations

HTTP webhooks for external tool calls. 5s timeout, retry with exponential backoff. OAuth2 coming soon (currently just API keys).

Pillar 4

Reliable Guardrails & Governance

Set boundaries your agents can't cross. Check outputs before they reach users. Track who changed what and when. Keep agents safe without slowing them down.

-0.3%
0.2%
Policy Violations
+5
42
Active Rules
-2
3
Risk Events
+124MB
2.1GB
Audit Log Size

Active Policies

PII Redaction (Regex-based)
Enforcedhigh
100%
Coverage
Rate Limiting (100 req/min)
Enforcedmedium
95%
Coverage
Output Length Cap (4096 tokens)
Enforcedhigh
98%
Coverage

Compliance Tracking

GDPRCompliant
Last audited: 2 days ago
SOC 2Compliant
Last audited: 1 week ago
ISO 27001In Progress
Last audited: 3 weeks ago

Guardrail Activity

Content FilterFP: 3
12
Blocked
1456
Allowed
Budget LimitFP: 0
5
Blocked
892
Allowed
Capability CheckFP: 1
8
Blocked
2341
Allowed

Recent Audit Events

Policy updated: PII Redaction5m ago
by admin@tc
Guardrail triggered: Budget exceeded12m ago
by system
Access denied: Unauthorized API call1h ago
by agent-42
Compliance check passed: GDPR2h ago
by system

Custom Rules Engine

Write rules in Python using our SDK. Pattern matching, NER-based filters, cost caps. Runs in isolated sandbox (still had one security incident last month).

Real-time Monitoring Dashboard

See violations as they happen. Filter by severity, agent, time range. Export logs for compliance reports. CSV only right now (JSON export pending).

Multi-tenant Isolation

Policies per workspace. Agents can't access other tenants' data. Database-level row security. Working on adding org-level policies next quarter.

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