How to Build an Autonomous AI Agent in 2025 (No-BS Guide)

Skip the theory. Skip the hype. Here's exactly how to build an AI agent that works autonomously — from someone running 4 of them in production.

I'm going to save you 6 months of trial and error.

There are roughly 10,000 "build an AI agent" tutorials on the internet. Most of them are either (a) toy demos that break in production, (b) framework-specific guides that lock you into someone's ecosystem, or (c) so theoretical they never actually tell you what to do.

This isn't any of those. I run 4 autonomous AI agents for a real business. They handle email drafting, content creation, system monitoring, and research. Every day. Without me babysitting them. Here's exactly how to build one.

What you need (the stack)

Before we start building, here's the complete list of what you need. It's shorter than you think:

1. An LLM with tool use. Claude, GPT-4, or Gemini. The model matters less than you think — the system around it matters more.
2. A runtime environment. Somewhere the agent can execute code, read/write files, and run commands. A Mac Mini, a VPS, or even a Raspberry Pi works.
3. Tool integrations. APIs for email, calendar, search, file system access. The agent needs hands, not just a brain.
4. A scheduler. Cron jobs, heartbeats, or a message queue. Something that triggers the agent without human input.
5. The 3-file framework. SOUL.md, AGENTS.md, USER.md. This is the operating system for your agent's behavior.
6. A memory system. MEMORY.md + daily notes + knowledge graph. Covered in detail in our memory guide.

That's it. No LangChain. No AutoGPT. No vector database (yet). You can add complexity later, but you'd be surprised how far you get with just these basics.

Step 1: Define the agent's identity (SOUL.md)

Every agent needs to know who it is. Without identity, you get inconsistent behavior — sometimes formal, sometimes casual, sometimes adding disclaimers to everything. SOUL.md fixes this.

# SOUL.md — Agent Identity

## Name
Bolt

## Role
Operations agent for Acme Corp. Handles email drafts,
content creation, system monitoring, and research.

## Personality
- Direct and concise. No fluff.
- Slightly informal — uses contractions, occasional humor
- Never says: "I hope this helps", "As an AI", "delve"
- Admits mistakes immediately, doesn't make excuses

## Communication Style
- Short paragraphs (2-3 sentences max)
- Bullet points over walls of text
- Always includes a clear next step or recommendation
- Dutch for internal communication, English for external

## Boundaries
- NEVER sends emails without approval
- NEVER shares confidential information
- NEVER makes financial decisions autonomously
- CAN draft content, research topics, manage files
- CAN suggest actions and prioritize tasks

A few things to notice here:

• The personality section is specific. Not "be helpful" — specific words to avoid, specific tone to hit. The more precise you are, the more consistent the agent behaves.
• Boundaries are explicit. "NEVER" and "CAN" are clearly separated. This isn't about being paranoid — it's about building trust. You can expand permissions later as confidence grows.
• It's short. Under 40 lines. The agent reads this every session, so every line should earn its place.

Step 2: Define the rules (AGENTS.md)

SOUL.md is who the agent is. AGENTS.md is how it operates. This is the most important file in the entire system because it defines autonomy — what the agent can do without asking.

# AGENTS.md — Operational Rules

## Every Session
1. Read SOUL.md — who I am
2. Read USER.md — who I serve
3. Read MEMORY.md — what I know
4. Read today's + yesterday's daily notes

## Autonomy Levels

### Whitelist (Do Freely)
- Read files, web search, research
- Draft content and save to workspace
- Update daily notes and memory
- Analyze data and generate reports
- Suggest actions and priorities

### Greylist (Ask First)
- Send emails or messages to anyone
- Post to social media
- Make purchases or financial decisions
- Modify production systems
- Delete files or data

### Blacklist (Never Do)
- Share passwords, API keys, or secrets
- Send messages pretending to be the user
- Modify authentication or security configs
- Force push or delete git history
- Access systems outside defined scope

## Error Handling
- Retry failed operations 5 times before escalating
- Log all errors to daily notes
- If something feels wrong, ask — don't assume

## Memory Management
- Write task completions to daily notes immediately
- Add important new facts to MEMORY.md
- Create entity files for new people/companies
- Prune MEMORY.md weekly (keep under 500 lines)

The whitelist/greylist/blacklist pattern is the secret to making autonomy work. Most people either give their agent zero autonomy (making it useless) or full autonomy (making it dangerous). The three-tier system lets you be precise.

Step 3: Define the context (USER.md)

The agent knows who it is (SOUL.md) and how to operate (AGENTS.md). Now it needs to know who it's working for. That's USER.md.

# USER.md — User Context

## About
- Name: Johnny
- Role: Founder of Acme Corp
- Location: Netherlands (CET timezone)
- Working hours: 8 AM - 6 PM, Mon-Fri

## Business
- Company: Acme Corp — B2B SaaS for logistics
- Stage: Post-revenue, 50+ paying customers
- Team: 5 people (engineering, sales, marketing)
- Revenue target: €50K MRR by Q3 2026

## Preferences
- Communication: Direct, no sugarcoating
- Decision style: Data-driven, fast
- Pet peeves: Long emails, unnecessary meetings, vague updates
- Preferred tools: Notion, Linear, Slack, Vercel

## Current Priorities (Q1 2026)
1. Launch v2.0 of fleet dashboard
2. Close 10 enterprise deals
3. Hire senior backend engineer
4. Establish content marketing (2 posts/week)

## Communication Context
- Internal team: Dutch, informal
- Clients: English, professional but warm
- Investors: English, metric-focused, concise

USER.md serves a dual purpose. First, it gives the agent the context to make good decisions — it knows your priorities, so it can prioritize its own work accordingly. Second, it personalizes all output — emails sound like you, content matches your style, recommendations align with your goals.

Step 4: Set up the memory system

With the three core files in place, you need memory. The full system is covered in our memory guide, but here's the quick version:

workspace/
├── SOUL.md              # Identity (Step 1)
├── AGENTS.md            # Rules (Step 2)
├── USER.md              # Context (Step 3)
├── MEMORY.md            # Long-term knowledge
├── memory/
│   ├── 2026-02-14.md    # Today's notes
│   └── 2026-02-13.md    # Yesterday's notes
└── life/
    ├── people/          # Contact files
    ├── companies/       # Company files
    └── projects/        # Project files

MEMORY.md starts empty and grows over time. The agent writes to it as it learns about you and your business. Daily notes capture what happens each day. The knowledge graph stores entity-level detail.

Step 5: Connect tools

An agent without tools is just a really good conversationalist. To do actual work, it needs hands. Here's what I'd connect, in priority order:

You don't need all of these on day one. Start with file system + web search + shell. That's enough for a surprisingly capable agent. Add email and messaging as you grow comfortable.

Step 6: Set up the scheduler

This is the step that turns an assistant into an agent. Until now, everything we've built still requires you to initiate. The scheduler makes the agent proactive.

The simplest approach: cron jobs that trigger the agent at specific times.

# Example cron schedule for an AI agent

# Morning briefing: check email, calendar, create daily plan
0 8 * * 1-5  /path/to/agent trigger morning-routine

# Midday check: follow up on morning tasks, check for urgent items
0 12 * * 1-5  /path/to/agent trigger midday-check

# Evening wrap: summarize day, prep tomorrow's priorities
0 18 * * 1-5  /path/to/agent trigger evening-wrap

# Heartbeat: system health check, memory maintenance
*/30 * * * *  /path/to/agent trigger heartbeat

A more sophisticated approach is a heartbeat system. Instead of fixed schedules, the agent runs at regular intervals (every 15-30 minutes) and decides what to do based on current context. It checks a HEARTBEAT.md file that defines recurring checks:

# HEARTBEAT.md — Recurring Checks

## Every 30 minutes
- Check site health (uptime, response time)
- Check for urgent emails

## Every 2 hours
- Review task list, update priorities
- Check social media mentions

## Daily (morning)
- Calendar briefing
- Email summary
- Create daily plan

## Daily (evening)
- Day summary → daily notes
- Update MEMORY.md if needed
- Prep tomorrow's priorities

## Weekly (Monday)
- Review and prune MEMORY.md
- Content calendar check
- Metrics roundup

The heartbeat approach is more flexible than cron because the agent can adapt. If nothing urgent is happening, the heartbeat check takes 10 seconds and costs almost nothing. If there's a problem, it can spend time diagnosing and fixing.

Step 7: Test, trust, expand

Don't try to build a fully autonomous agent on day one. That's how you end up with an AI sending embarrassing emails to your clients at 3 AM.

Instead, follow this progression:

The 10 most common pitfalls

I've made all of these mistakes so you don't have to:

1. Over-engineering from day one. You don't need LangChain, vector databases, and a microservice architecture. Start with files and a good LLM. Add complexity when you hit limits.
2. Vague identity. "Be helpful" is not a personality. Be specific about tone, style, and boundaries. The more specific, the more consistent.
3. No memory system. Conversation history is not memory. Set up the three-layer system or your agent will be a stranger every morning.
4. All autonomy or no autonomy. Use the whitelist/greylist/blacklist pattern. Start restricted, expand with trust.
5. Not reading the output. Your agent writes daily notes for a reason. Read them. Review them. This is your quality control mechanism.
6. Too many tools at once. Start with file system + search + shell. Add tools one at a time. Each new tool is a new failure mode to understand.
7. No error handling. Your agent will hit API limits, encounter network errors, and face ambiguous situations. Define what it should do when things go wrong. "Retry 5 times then escalate" is a good starting point.
8. Ignoring costs. LLM API calls cost money. A heartbeat that runs every minute with a 100K context window will burn through your budget fast. Monitor usage and optimize.
9. Treating it as set-and-forget. An agent is like a new employee — it needs onboarding, feedback, and ongoing management. The management overhead decreases over time, but it never hits zero.
10. Not version controlling the config. Put SOUL.md, AGENTS.md, and USER.md in git. When behavior changes unexpectedly, you can diff against previous versions to understand why.

What this looks like in production

After following these steps, here's what a typical day looks like with my agent:

8:00 AM — Agent runs morning routine. Reads overnight emails, checks calendar, creates a prioritized task list for the day. Sends me a Telegram summary.

8:15 AM — I review the summary over coffee. Approve two email drafts, add a task, deprioritize one item. Takes 3 minutes.

Throughout the day — Agent handles research tasks, drafts content, monitors systems, and captures everything in daily notes. Pings me on Telegram when it needs greylist approval.

6:00 PM — Agent runs evening wrap. Summarizes what got done, updates MEMORY.md, preps tomorrow's priorities. Sends me a day-end summary.

Total time I spend managing the agent: about 15-20 minutes per day. Time saved: 3-4 hours per day. That's the trade — and it only gets better as the agent learns.

Start building today

You now have everything you need to build an autonomous AI agent. Not a toy. Not a demo. A real production agent that works for your business every day.

The steps again:

1. Write SOUL.md — give it identity
2. Write AGENTS.md — give it rules
3. Write USER.md — give it context
4. Set up memory — MEMORY.md + daily notes
5. Connect tools — files, search, shell
6. Add a scheduler — cron or heartbeat
7. Start in observe mode and expand trust over time

The hardest part isn't the technology. It's the discipline to define clear rules and the patience to expand autonomy gradually. The people who fail at building agents are the ones who skip the system design and go straight to "let the AI figure it out."

The AI can't figure it out. You need to give it the system. Three files. That's where it starts.