Agent Fleet

AI agents, purpose-built for your infrastructure

Your DevOps team is firefighting, not building. Pipeline failures at 2 AM. Database incidents requiring tribal knowledge. Security audits that take weeks of manual review.

Agent Fleet: Specialized AI agents that understand your infrastructure. Not generic chatbots — purpose-built agents with real access to your Planton resources, trained on your specific stack.

agent session

▶ Agent: pipeline-debugger
Trigger: CI failure on main
 
⚙ Tool: fetch_pipeline_logs
→ Build step 3/5 failed: OOM at 512Mi
 
⚙ Tool: get_resource_spec
→ Current memory limit: 512Mi
→ Peak usage (7d): 480Mi
 
💡 Recommendation:
Increase memory limit to 768Mi
→ planton apply -f updated-spec.yaml

0

Generic Chatbots

100%

Purpose-Built

0

Simulated Responses

100%

Real Execution

Skills

Agents learn your organization's patterns. Create skills that encode your operational knowledge into repeatable, automatable procedures.

Encode runbooks, naming conventions, and compliance requirements as skills

Your practices, not generic best practices — tailored to your stack

Version-controlled skill definitions alongside your infrastructure code

Composable skills that agents combine for complex scenarios

Agent Skills

# runbook.skill.yaml
name: postgres-failover
trigger: alert.postgres.replication_lag > 30s

steps:
  - check_replica_status
  - verify_data_consistency
  - promote_replica
  - update_connection_strings
  - notify_oncall_channel

Purpose-built agents for every infrastructure challenge

From marketplace discovery to production orchestration — Agent Fleet gives your team AI-powered infrastructure operations.

Marketplace

Browse and deploy agents for specific tasks. Pipeline troubleshooting, database management, security hardening, drift detection.

pipeline-debugger
security-auditor
drift-detector
db-optimizer

Sub-Agents & Orchestration

Complex tasks broken into specialized sub-tasks. A deployment agent invokes security, testing, and notification agents automatically.

delegate
retry
context-pass
approval-gate

MCP Integration

Agents connect through Model Context Protocol. Real tool execution against your infrastructure, not simulated responses.

⚙ Tool: kubectl_get_pods
  namespace: backend
  cluster: prod-us-east-1

→ 3 pods running, 0 pending
→ No restarts in 24h

Testing

Test suites for agent validation. Verify behavior before deploying to production with structured test scenarios.

12 passed

Test suite

0 failed

Regressions

Full visibility into every agent action

Real-time streaming of tool calls, reasoning steps, and results. Every action is auditable and replayable.

agent session — ses-7f2a9c

▶ Agent: pipeline-debugger
Session: ses-7f2a9c
Trigger: CI failure on main
 
⚙ Tool: fetch_pipeline_logs
→ Build step 3/5 failed: OOM at 512Mi
 
⚙ Tool: get_resource_spec
→ Current memory limit: 512Mi
→ Peak usage (7d): 480Mi
 
💡 Recommendation:
Increase memory limit to 768Mi
→ planton apply -f updated-spec.yaml

Live Streaming

Every tool call, decision, and result streamed in real time to the console.

Session Replay

Replay any session for debugging and post-incident review. Full timeline preserved.

Exportable Logs

Export session logs for compliance reporting and external audit trail integration.

Stop firefighting

Let agents handle the toil

Deploy your first agent in minutes. Purpose-built for your infrastructure, with real access to your Planton resources.


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