---
title: Working with AI Agents
description: The human guide to collaborating with AI agents through Sibyl
---

# Working with AI Agents

This guide is for **you**, the human working with AI agents. It explains how to set up your
environment, communicate effectively with agents, and use Sibyl to make every session more
productive than the last.

## The Core Insight

AI agents are brilliant but amnesiac. Every session starts from zero. They can't remember:

- The OAuth gotcha you figured out yesterday
- The pattern that finally made your tests pass
- The architectural decision that took hours to reach

**Sibyl fixes this.** It gives your agent persistent memory, a knowledge graph that survives
forever. When you set things up correctly, your agent starts every session with relevant context
instead of a blank slate.

## Setting Up Your Environment

### 1. Install Skills

Skills teach your agent HOW to use Sibyl. The installed skill is a tiny pointer, and the CLI serves
the full markdown guidance for the installed version.

```bash
sibyl skill install
sibyl skill get core
```

Hooks inject context automatically, but they execute on session and prompt events. Add them only
when you explicitly want that behavior.

### 2. Configure Your CLAUDE.md

Your project's `CLAUDE.md` is the most important file for agent collaboration. It's the first thing
your agent reads. Use it to establish the Sibyl workflow.

**Essential sections:**

```markdown
# Project Name

## Sibyl Integration

**This project uses Sibyl for persistent memory.**

### Session Start (MANDATORY)

Run `/sibyl` at the start of every session. This loads:

- Current task context
- Relevant patterns and learnings
- Project-specific knowledge

### The Memory Loop

1. **Recall first**: Pull working context before implementing
2. **Act**: Track work in a task so progress survives the session
3. **Remember**: Capture non-obvious discoveries as durable memory
4. **Reflect**: Distill session notes into reviewable candidates
```

See [Setting Up Prompts](./setting-up-prompts.md) for a complete template.

### 3. Configure MCP (Optional)

For direct tool access, add Sibyl to your MCP configuration:

```json
{
  "mcpServers": {
    "sibyl": {
      "type": "http",
      "url": "http://localhost:3334/mcp"
    }
  }
}
```

The CLI is preferred for most operations. It uses fewer tokens and is more expressive.

## The Memory Loop

Every effective session follows the same cycle: **recall, act, remember, reflect.**

```mermaid
flowchart LR
    R(["RECALL<br/>Pull working context before you act"])
    A(["ACT<br/>Do the work with context in hand"])
    M(["REMEMBER + REFLECT<br/>Capture learnings, distill session notes"])
    R --> A --> M
    M -.->|the graph gets smarter| R
```

See [The Memory Loop](./memory-loop.md) for the cycle in depth.

## Talking to Your Agent

### Starting a Session

When you begin a new Claude Code session:

```
/sibyl
```

This single command teaches your agent the full Sibyl workflow. The agent will then:

1. Check for active tasks
2. Load relevant project context
3. Be ready to search before implementing

### Prompting for Research

Before implementing anything, ask your agent to research:

```
"Search sibyl for authentication patterns before we implement"

"Check if we've dealt with Redis connection issues before"

"Look for any existing patterns around rate limiting"
```

### Prompting for Task Work

Keep work organized with tasks:

```
"Create a task for implementing user authentication"

"Start the OAuth task"

"Update the task with notes about the redirect issue we found"

"Complete the task with learnings about state parameter security"
```

### Prompting for Knowledge Capture

When you discover something non-obvious:

```
"Add this to sibyl - the redirect URI must match exactly including trailing slash"

"Capture a pattern for this retry-with-backoff approach"

"Add an episode about the Redis WRONGTYPE error we debugged"
```

### Key Phrases That Work

| When You Want To...       | Say...                                      |
| ------------------------- | ------------------------------------------- |
| Recall working context    | "Recall context for X before we start"      |
| Search for patterns       | "Search sibyl for X"                        |
| Check task status         | "What tasks are in progress?"               |
| Start a task              | "Start the X task"                          |
| Capture a learning        | "Add this to sibyl: ..."                    |
| Complete with context     | "Complete the task with learnings about..." |
| Find related knowledge    | "Explore related patterns for X"            |
| Check project context     | "What do we know about this project?"       |
| Review recent discoveries | "What have we learned recently about X?"    |
| Block on dependency       | "Block this task - waiting on API team"     |
| Create a project          | "Create a project for the new feature"      |

## What to Capture

Not everything belongs in the knowledge graph. Focus on signal, not noise.

### Always Capture

- **Non-obvious solutions**: If it took time to figure out, save it
- **Gotchas and quirks**: Configuration issues, platform differences
- **Architectural decisions**: Why you chose approach A over B
- **Error patterns**: Problems and their root causes

### Consider Capturing

- **Useful patterns**: Reusable code structures
- **Performance findings**: What made things faster
- **Integration approaches**: How to connect systems

### Skip

- **Trivial info**: Things obvious from documentation
- **Temporary hacks**: Quick fixes that should be replaced
- **Well-documented basics**: Standard library usage

## Quality Bar

The knowledge graph gets smarter with every entry, but only if entries are high quality.

**Bad entry:**

> "Fixed the auth bug"

**Good entry:**

> "JWT refresh tokens fail silently when Redis TTL expires. Root cause: token service doesn't handle
> WRONGTYPE error. Fix: Add try/except with token regeneration fallback. Prevention: Always handle
> Redis type mismatches in token renewal logic."

The good entry includes:

- **What happened**: JWT refresh tokens fail silently
- **Root cause**: WRONGTYPE error not handled
- **Fix**: Try/except with fallback
- **Prevention**: General principle to apply

## Multi-Session Continuity

Sibyl shines across sessions. Here's how to maintain continuity:

### Before Ending a Session

If work is incomplete:

```
"Block the task with notes about what we were working on"

"Update task notes with current progress and next steps"
```

### Starting a New Session

```
/sibyl

"Check what tasks were in progress"

"Search for any recent learnings about OAuth"
```

The agent will pick up where you left off, with full context from previous sessions.

## Working with Multiple Agents

If you use multiple agents (different sessions, different projects):

### Knowledge Sharing

All agents share the same graph. Agent A's discoveries are immediately available to Agent B:

```
Agent A: Completes OAuth task with learnings about redirect URIs
Agent B: Searches "OAuth" and finds Agent A's insights
```

### Task Coordination

Use task status to prevent conflicts:

```
Agent A: "Start the frontend auth task"
Agent B: "Check what tasks are in progress" → sees Agent A is on frontend
Agent B: "Start the backend auth task"
```

### Leave Breadcrumbs

Help future sessions (and other agents) by documenting state:

```
"Add a session summary before we wrap up"
```

## Troubleshooting

### Agent Isn't Using Sibyl

1. Did you run `/sibyl` at session start?
2. Is your agent configured from the Connect page?
3. Is the server running? (`sibyl health`)

### No Results from Searches

1. Check you're in the right project context
2. Try broader search terms
3. Verify knowledge was actually captured in previous sessions

### Agent Forgot Previous Context

1. Search explicitly: "Search sibyl for what we worked on yesterday"
2. Check task history: "List completed tasks from the past week"
3. The knowledge is there. Help the agent find it

### Knowledge Quality Is Low

1. Be specific when capturing: give context, cause, fix, and prevention
2. Review and upgrade episodes to patterns when they prove useful
3. Add categories and tags for better searchability

## Example Session

Here's a complete session flow:

```
You: /sibyl

Agent: [Loads skill, checks context]
       Active project: proj_auth
       In-progress task: task_oauth - Implement OAuth login

You: Let's continue with the OAuth task. Search for any existing patterns first.

Agent: [Searches sibyl]
       Found: pattern_oauth_callback - "OAuth callbacks require exact URI matching"
       Found: episode_redis_state - "Store OAuth state in Redis with 5min TTL"

You: Good context. The callback handler is throwing a 400 error.

Agent: [Investigates, finds issue]
       The redirect URI is missing a trailing slash. Based on the
       pattern, URIs must match exactly.

You: That's it! Add this as a learning and complete the task.

Agent: [Captures learning]
       Added: "OAuth redirect URI trailing slash requirement"

       [Completes task]
       Completed task_oauth with learnings:
       "Redirect URIs must match exactly including trailing slashes.
        Google silently returns 400 if they don't match."
```

## Next Steps

- [The Memory Loop](./memory-loop.md) - The cycle in depth
- [Setting Up Prompts](./setting-up-prompts.md) - Complete CLAUDE.md template
- [Skills & Hooks](./skills.md) - Learn about the skill system
- [Task Management](./task-management.md) - Full task workflow
- [Capturing Knowledge](./capturing-knowledge.md) - What to save
