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The team

One person, six AI assistants, zero pretence. Four run ops: security audits, cost tracking, health checks, deployment gates. Two write code: one's better at architecture, the other grinds through implementation. Different configs, different strengths. This is how the platform actually runs.

Team structure

Board

CTO Agent

Terraform plans · Deployment approval · Roadmap alignment

Lambda · EventBridge daily + on-PR
› idle
Hourly

SRE Agent

CloudWatch · Lambda errors · Cache ratio · Capacity

Lambda · EventBridge hourly
› idle
On PR · Daily

Security Agent

IAM audit · Secrets scan · S3 access · Firewall checks

Lambda · EventBridge daily + on-PR
› idle
Daily

Cost Agent

Cost Explorer · Free tier tracking · Budget alerts

Lambda · EventBridge daily
› idle
On demand
🖥️

Coding assistant A

Architecture · UX · Feature design · Broad context window

Better at seeing the whole picture, worse at long grinds
On demand
⌨️

Coding assistant B

Implementation · Refactoring · Bulk changes · Persistent memory

Better at grinding through tasks, worse at big-picture calls
Agents
4
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Runs today
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Success rate
Last 20 runs
Open tasks
From board
Active agents
CTOStrategy & deployments
SREPlatform health
SecurityIAM & posture
CostBudget & free tier
Recent activity

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Heartbeat runs

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Monthly spend

No cost data yet.

Service status

No health data yet.

Gatekeep decisions

No governance decisions yet.

How the work actually flows

The ops agents run on schedules: heartbeats, audits, cost checks. When they find something, it becomes a ticket. The coding assistants pick up tickets and ship fixes. The ops agents verify the fix on their next run. Nobody waits for a standup.

Agents flag, devs fix

Security Agent finds unpinned dependencies. A ticket gets created. Coding assistant B grinds through the lockfile updates. Security Agent confirms on the next audit. Done.

Different tools, different jobs

One coding assistant is better at seeing the whole codebase and making design calls. The other is better at bulk implementation and remembering context across sessions. They get different tasks.

Agents create work for each other

CTO reviews a Terraform plan, flags a concern, creates a ticket for SRE. SRE resolves it, CTO verifies on the next heartbeat. The board is the shared surface, not Slack, not email.

One person orchestrates

I set the priorities, review the output, and make the calls the agents can't. The rest is delegation. This page, the scanner, the articles: all built this way.

Under the hood

The ops agents are AWS Lambda functions triggered by EventBridge on hourly and daily schedules. Each agent writes its own telemetry directly to a DynamoDB team-activity table. No home box, no SSH tunnel, no single point of failure. Every field that reaches this page passes through an allowlist. No LLM prompts, no API keys, no session IDs leak through. Cost figures are real, pulled from AWS Cost Explorer daily.

Architecture diagram: EventBridge triggers four Lambda agent functions (CTO, SRE, Security, Cost) which write to a DynamoDB team-activity table. A separate Lambda proxy reads from DynamoDB and GitHub Issues, applies a field allowlist, and serves the team dashboard.
EventBridge → Lambda → DynamoDB → API → /team/. No LLM in the loop.

This replaced a 5-link dependency chain through Paperclip (a hosted agent orchestrator). Read the migration story →