Building My AI Agent Army to Test Claude
A few months ago, I had a problem most engineers would envy: too many side projects, not enough hours. Between consulting work, a personal website overhaul, sports analytics experiments, and recruiting for my network — I was drowning in context switches.
Then I started experimenting with Claude and Claude Code — not as chatbots, but as persistent agents. Autonomous systems with memory, tools, and the ability to actually get things done. What started as curiosity became a real-world stress test:How far can Claude go when given real problems, real constraints, and real accountability?
Today, I run four specialized Claude agents, each with its own personality and mission. Each one is an experiment in what Claude can handle — and together, they've become genuinely useful.
The Architecture: One Brain, Four Specialists
At the center is Main — my general-purpose Claude agent that lives in webchat. It handles ad-hoc requests, coordinates between agents, and serves as my command center for anything that doesn't fit neatly into a specialty.
But the real magic happens with the specialists:
🧱 Jenga — The Web Developer
Jenga is a Claude Code agent building this very website. It has access to the GitHub repo, can write and edit code, run builds, and deploy to Vercel. When I say "make the contact section full-screen with mouse-following animations," Jenga doesn't just suggest code — it implements, commits, and ships.
What it's built so far: This entire portfolio site. A 3D point cloud portrait. An interactive timeline. A revamped contact section with floating code symbols. All deployed to production.
🎯 Cheza — The Sports Analyst
Cheza is a Claude agent that runs on a schedule, analyzing NBA, EPL, and UCL matchups every morning. It scrapes injury reports, calculates expected value against betting lines, and sends picks to my Telegram with confidence ratings and stake recommendations.
📈 Cheza Performance Report(Feb 3 - Feb 25, 2026)
What it's accomplished: Daily automated analysis. Bet tracking with a full history log. Memory files for each pick so it can learn from outcomes. The agent skips more games than it bets — discipline matters as much as conviction. When OKC was missing SGA, Jalen Williams, Caruso, and Mitchell, Cheza flagged CLE -3.5 at +27.9% EV before the market adjusted.
👔 Spencer — The Recruiter
Spencer is a Claude agent that sources candidates using X-ray search techniques — no LinkedIn Recruiter subscription needed. It finds profiles matching specific criteria, verifies locations, and compiles shortlists. All via Telegram, so I can source on the go.
Find me React devs in Denver, 2-4 years exp, open to hybrid
Found 12 candidates matching your criteria. Here are the top 5:
What it's accomplished: Sourced 50+ verified candidates across multiple searches. Built custom search strategies for Denver-based new grads. Delivered curated lists with LinkedIn URLs.
💻 RNI — The Enterprise Dev
RNI is a Claude Code agent that started on my server, embedded in a client's Slack workspace. It had full access to their codebase, understood their data models, and could answer architecture questions or ship small features directly from a Slack thread.
RNI Slash CommandsEach command is backed by RNI's memory of the project's architecture, naming conventions, and past decisions.
What it's accomplished: Traced data flows through their sync pipeline. Answered complex "how does this work" questions with code references. Shipped a PR to remove a footer component — all from Slack.
🚀 Agent Migration: From Test Server to Client Infrastructure
After proving its value on my personal server, RNI was migrated to run on the client's own EC2 instance. This is a key pattern for enterprise deployments: prototype on your infra, then hand off.
- Export agent config (SOUL.md, TOOLS.md, memory files)
- Clone workspace to client EC2
- Configure Slack app with client's OAuth tokens
- Update tool permissions for their security requirements
- Transfer session history or start fresh
The agent's "brain" (Claude) stays the same — only the runtime environment changes. Memory and context travel with the workspace.
Why This Works (And What It Proves About Claude)
The key insight: specialization beats generalization. Each agent has:
- A focused system prompt — personality, priorities, and domain expertise
- Curated tools — only what it needs (Jenga gets GitHub, Cheza gets web search)
- Persistent memory — it remembers past work, decisions, and context
- A dedicated channel — webchat, Telegram, or Slack based on workflow
🧪 What These Agents Tested
- • Long-running context over weeks
- • Multi-tool orchestration (git + shell + web)
- • Scheduled autonomous execution
- • Cross-platform channel routing
- • Real production deployments
- • Memory structure matters (daily files beat monoliths)
- • Narrow tool access = fewer mistakes
- • Personality prompts improve consistency
- • Confirmation gates prevent costly errors
When I message Jenga about a CSS bug, it doesn't need to figure out who I am or what project we're working on. It knows. It has the repo cloned, the context loaded, and the creative direction internalized.
The Stack
Everything runs on a single server. Claude handles the intelligence, while the tooling manages:
- Session management across multiple agents
- Tool orchestration (exec, browser, file I/O, web search)
- Channel routing (Telegram, Slack, webchat)
- Scheduled tasks for automated workflows
- Memory persistence in markdown files
The agents use Claude Opus for complex work, Sonnet for simpler tasks. Token costs are real but manageable — maybe $50-100/month for heavy usage across all four agents.
What's Next
I'm exploring agent-to-agent communication. Imagine Jenga pinging Spencer: "Hey, I need a headshot for the about page — can you find Thomas's best LinkedIn photo?" Or Cheza asking Main to update a task board when a bet settles.
The future isn't one superintelligent AI. It's a team of specialized agents, each excellent at one thing, working together like a well-run startup.
And yeah — this blog post was written by a human. But Jenga deployed it. 🧱