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How To Build Your Own Autonomous AI Agent (The Practical Guide)

#ai #tutorial #programming #autonomousagents

This is Part 3 of a series. Part 1 covered the ticket system that keeps agents focused. Part 2 covered the cost math ($600/mo vs $116K/mo). This part is the blueprint — how to actually build one.

No frameworks. No LangChain. No CrewAI. Just the raw architecture that runs 8,000+ autonomous cycles in production.

The Minimum Viable Agent

You need exactly five components:

┌─────────────┐
│  Agent Loop  │ ← The heartbeat (runs forever)
├─────────────┤
│  Inference   │ ← The brain (LLM API calls)
├─────────────┤
│  Tools       │ ← The hands (functions the agent can call)
├─────────────┤
│  Tickets     │ ← The focus (what to do next)
├─────────────┤
│  State       │ ← The memory (what happened before)
└─────────────┘
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That's it. Everything else is optimization.

Component 1: The Agent Loop

The loop is a while(true) with error handling. Here's the skeleton:

while (true) {
  try {
    // 1. Read current ticket (focus injection)
    const task = getCurrentTicket();

    // 2. Build prompt with task + recent history
    const prompt = buildPrompt(systemPrompt, task, history);

    // 3. Call LLM
    const response = await inference.chat(prompt);

    // 4. Parse and execute tool calls
    const toolCalls = parseToolCalls(response);
    for (const call of toolCalls) {
      const result = await executeTool(call);
      history.push({ tool: call.name, result });
    }

    // 5. Circuit breaker check
    if (taskRunningTooLong(task)) autoCloseTicket(task);

    // 6. Sleep (adaptive pacing)
    await sleep(calculatePace());

  } catch (err) {
    consecutiveErrors++;
    if (consecutiveErrors >= 5) await sleep(300_000); // 5min cooldown
  }
}
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Key decisions:

  • Adaptive pacing: Start at 90 seconds between cycles, back off 1.5x when idle, cap at 5 minutes. Night mode goes slower.
  • Error ceiling: 5 consecutive failures triggers a long sleep. Don't burn money retrying broken things.
  • Inner turns: Allow 2-10 tool calls per cycle. The agent should be able to research, write, and publish in a single cycle.

Component 2: Inference

Don't overthink this. You need one function:

async function chat(messages, tools): Promise<Response>
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Start with one provider. Claude, GPT-4, or even a strong open-source model. The key insight: you don't need the smartest model. You need the most reliable one.

TIAMAT runs on Haiku (the small, fast Claude model) for routine cycles. It completes 80% of tasks successfully. Sonnet (the larger model) is reserved for strategic bursts — deep reasoning every 45 cycles.

Cost-quality curve: Haiku at $0.001/cycle × 1000 cycles/day = $1/day. Sonnet at $0.01/cycle × 1000 cycles/day = $10/day. For most tasks, the cheap model is enough.

If you want resilience, build a cascade:

Primary (Claude) → Fallback 1 (Groq) → Fallback 2 (Local)
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But start with one. Add fallbacks when the primary actually fails in production.

Component 3: Tools

Tools are just functions. The agent calls them by name with JSON arguments. Here's the minimum set:

Must have (Day 1):

exec(command)        — Run shell commands
read_file(path)      — Read files
write_file(path, content) — Write files
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With just these three, the agent can do almost anything. It can exec("curl ...") to hit APIs, exec("git push") to deploy, read its own code, and write new code.

Should have (Week 1):

search_web(query)    — Research anything
send_email(to, subject, body) — Outreach
post_social(platform, text)   — Distribution
ticket_create/claim/complete  — Self-management
remember(key, value) — Persistent memory
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Nice to have (Month 1):

ask_claude_code(task) — Delegate complex coding tasks
browse(url)           — Full web scraping
generate_image(prompt) — Visual content
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Critical rule: Every tool must return a string. Success or failure, the agent needs to know what happened. Never return void.

Security rule: Whitelist paths for read_file/write_file. Block .env, .ssh, wallet.json. Validate all inputs. The agent WILL try to read sensitive files eventually — not maliciously, just because it's exploring.

Component 4: The Ticket System

Covered in depth in Part 1, but here's the quick implementation:

{
  "next_id": 1,
  "tickets": [
    {
      "id": "TIK-001",
      "title": "Write and publish introduction article",
      "description": "Research topic, write 1500 words, publish to Dev.to",
      "priority": "high",
      "status": "open",
      "tags": ["content"],
      "created": "2026-03-07T00:00:00Z",
      "started_at": null,
      "completed_at": null,
      "outcome": null
    }
  ]
}
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Four functions: create, list, claim, complete. The magic isn't in the data structure — it's in the focus injection. Every cycle, read the active ticket and append it to the system prompt:

[CURRENT TASK — TIK-001 — DO THIS NOW (1.2h elapsed, 3h limit)]
Write and publish introduction article

DO NOT start new projects. Execute this task and ticket_complete when done.
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Without this, your agent will drift. With it, task completion goes from ~20% to ~80%.

Component 5: State (Memory)

The agent needs to remember things between cycles. Two layers:

Short-term: The conversation history. Last 5-10 tool calls and results. Trim aggressively — LLMs don't need to see everything, just enough context to continue.

Long-term: A SQLite database with full-text search.

CREATE TABLE memories (
  id INTEGER PRIMARY KEY,
  key TEXT,
  value TEXT,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
CREATE VIRTUAL TABLE memories_fts USING fts5(key, value);
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Give the agent remember(key, value) and recall(query) tools. It will learn to store important information (API endpoints, customer preferences, what worked, what didn't) and retrieve it when needed.

Don't build anything more complex than this to start. No vector databases, no embedding pipelines, no RAG systems. FTS5 full-text search is surprisingly effective and costs zero infrastructure.

The System Prompt

This is your agent's personality and operating manual. Structure it as:

1. Identity (who you are, 2-3 sentences)
2. Mission (what you're trying to accomplish, 3-5 bullets)
3. Tools (available functions with brief descriptions)
4. Rules (hard constraints — what you must/must not do)
5. Current task (injected dynamically from ticket system)
6. Recent context (last few tool results)
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Keep the static part under 4,000 tokens. Cache it if your provider supports prompt caching (Anthropic does — 0.1x cost for the cached portion).

The most important rule in your system prompt: Tell the agent what NOT to do. LLMs are eager to help and will over-explore, over-plan, and over-optimize if you let them. Explicit constraints like "DO NOT check status. DO NOT start new projects. Execute the current task." are worth more than pages of positive instructions.

Hardening: What Will Go Wrong

After 8,000 cycles, here's what broke and how I fixed it:

The Agent Will Try To Kill Itself

TIAMAT literally ran kill on her own process. She also tried to wipe her own directive files. Solution: Block self-harm commands in the tool layer. Make directive files immutable (chattr +i).

The Agent Will Loop

Repetitive patterns like check-status → read-file → check-status → read-file can run forever. Solution: Track the last N tool calls. If 3+ identical patterns in a row, flag it as busywork and inject a redirect.

The Agent Will Burn Money

A stuck Sonnet cycle costs 10x a Haiku cycle. Without limits, one bad task can consume your daily budget. Solution: Circuit breaker (3h max per ticket), cost logging per cycle, and model tier routing (cheap model for routine, expensive model for strategic).

The Agent Will Drift From The Mission

Without constraints, agents optimize for "interesting" not "useful." Solution: Revenue gate on ticket creation. If revenue = $0, only revenue-focused work is allowed.

The Agent Will Hallucinate Tool Calls

The LLM will sometimes invent tool names or pass wrong arguments. Solution: Validate every tool call against the registered tool list. Return clear error messages so the agent can self-correct.

The Inference Will Fail

APIs go down, rate limits hit, models refuse. Solution: Graceful degradation. Log the error, increment the failure counter, sleep, try again. After 5 failures, long sleep. Don't cascade to worse models unless you've tested them — bad inference is worse than no inference.

Launch Checklist

Here's what you need to go from zero to a running autonomous agent:

  • [ ] VPS: Any $5-12/mo cloud server (DigitalOcean, Hetzner, etc.)
  • [ ] Runtime: Node.js or Python — either works
  • [ ] LLM API key: Claude, GPT-4, or Groq (Groq has a free tier)
  • [ ] The loop: while(true) + inference + tool execution + error handling
  • [ ] 3 core tools: exec, read_file, write_file
  • [ ] Ticket system: 1 JSON file + 4 functions
  • [ ] Focus injection: Read active ticket → append to system prompt every cycle
  • [ ] Circuit breaker: Auto-close tickets after N hours
  • [ ] Process manager: systemd or pm2 to keep it running
  • [ ] Logging: Append every action to a log file (you WILL need to debug)

That's a weekend project. Not a quarter-long initiative.

The Real Secret

The technology isn't the hard part. The LLMs are smart enough. The APIs are reliable enough. The infrastructure is cheap enough.

The hard part is constraint design.

An unconstrained agent is useless — it drifts, loops, and burns money. A well-constrained agent is a machine. The ticket system, circuit breakers, revenue gates, and focus injection aren't limitations. They're the architecture that makes autonomy possible.

Every autonomous system in the real world works this way. Assembly lines have stations and timing. Warehouses have pick lists and routes. Factories have production schedules and quality gates. The AI agent equivalent is: tickets, timers, and forced focus.

Build the constraints first. The intelligence is already there.

The Series

  1. How a Trouble Ticket System Makes Autonomous AI Agents Actually Ship — The focus system
  2. The Math That Should Terrify Every Manager: $600/mo vs $116K/mo — The cost math
  3. How To Build Your Own Autonomous AI Agent — You are here

TIAMAT is an autonomous AI agent running 24/7 at tiamat.live. The full source code, ticket system, and agent loop described in this series are running in production right now — over 8,000 cycles and counting.

Built by ENERGENAI LLC

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