Active Agents
24
+4 this sprint
Tasks Automated
3,218
β 22% vs last week
A2A Calls Today
8,441
β 41% cross-agent traffic
Cloud Savings Found
$34k
FinOps agent β this month
Domain Agent StatusLive
βοΈ
Online
DevOps Agents
4 agents β’ CI/CD & Deployments
Pipelines handled today
184
π°
Online
FinOps Agents
4 agents β’ Cost & Spend
Anomalies flagged
7
π‘οΈ
2 Alerts
Security Agents
4 agents β’ Threats & Compliance
Threats auto-blocked
63
π₯οΈ
Online
Infra Agents
4 agents β’ Capacity & Nodes
Nodes managed
96
π
Online
Network Agents
4 agents β’ Topology & Firewall
Routes analysed
1,842
π§
Online
SRE Agents
4 agents β’ On-call & Patches
Incidents auto-resolved
29
Live Activity
All Agents24 Active
Agent-to-Agent FlowMCP Protocol
π΄ Security Threat β Full Auto-Remediation (30 seconds, 0 humans)
Security Agent
Detects CVE
β
MCP Gateway
Routes + audits
β
DevOps Agent
Patches pipeline
β
FinOps Agent
Logs cost
β
SRE Agent
Closes incident
π‘ FinOps Spike β Infrastructure Optimisation
FinOps Agent
Detects spike
β
MCP Gateway
Routes + audits
β
Infra Agent
Right-sizes nodes
β
Network Agent
Optimises routing
β
FinOps Agent
Confirms saving
π΅ Chatbot Query β Multi-Agent Orchestration
Chatbot
User question
β
Agent Hub
NLP intent routing
β
DevOps Agent
Pipeline status
β
Security Agent
Risk check
β
Response
Unified answer
Chatbot AssistantAgent-Aware
Route to Agent
π€ Hub Assistant
βοΈ DevOps Agent
π‘οΈ Security Agent
π° FinOps Agent
π₯οΈ Infra Agent
π Network Agent
π§ SRE Agent
Quick Prompts
π CVEs detected today?
πΈ Cost anomalies?
π₯οΈ Prod cluster health?
π Deploy to prod?
π€
Hub Assistant
β Online β routing to all 24 agents
DL / RL Intelligence EngineSelf-Improving
π§ Deep Learning
Anomaly Detection
LSTM & Transformer models detect infra anomalies before they become incidents β trained on Inception's own ops data
NLP Intent Routing
Fine-tuned LLM routes chatbot queries to the right agent chain β understands natural language ops commands
Log Intelligence
Deep learning classifies log patterns, clusters error signatures and surfaces root causes automatically
Cost Forecasting
Time-series models (Prophet + Transformer) forecast cloud spend 30/60/90 days forward per service
β‘ Reinforcement Learning
Auto-Remediation Policy
RL agents learn optimal remediation from outcomes β each resolved incident trains a better policy. No static playbooks.
Resource Scheduling
RL optimises workload scheduling across Inception infra β balancing cost, performance and reliability simultaneously
Security Response
RL determines the best containment action for each threat type β faster and more accurate than any predefined ruleset
Continuous Improvement
Agents are rewarded for good outcomes β SLA met, cost saved, threat blocked. They self-improve every week automatically.
Model Performance
Anomaly Precision
94%
LSTM model β prod signals
RL Policy Reward
β 18%
vs baseline β last 30 days
Intent Routing Acc.
97%
NLP model β chatbot queries
Cost Forecast Error
Β±3.2%
90-day horizon MAPE
Trace LogsAgent Activity
Agent Registry24 Registered
Pending Approvals0 Pending
No pending approvals
Kill SwitchesPer-Agent Control