Threads: your AI agent workbench

Build, train, and iterate on AI agents through natural conversation—then deploy to production-ready code

Threads is where ideas become agents. it’s a conversational interface that lets you build, train, and refine AI agents through natural language—no coding required. When you’re ready, deploy your agent as production-ready code.

Unlike traditional chat interfaces that forget everything when you close the tab, Threads creates persistent, purpose-built agents that remember their training and can be deployed as real software.

Why threads matters

Most AI agent development follows a painful pattern:

1. Business expert describes what they need
2. Developer interprets and codes a solution
3. Back-and-forth to get it right
4. Weeks later, maybe you have what you wanted

Threads eliminates this translation loss. Domain experts work directly with agents, training them through conversation, seeing immediate results, and iterating in real-time. When ready, eject to code exports your trained agent as a complete Modus app.

Traditional: Idea → Requirements → Code → Deploy → Test → Repeat
Threads:     Idea → Conversation → Working Agent → Eject to Code

Core concepts

Agents, not chats

Every conversation in Threads is building toward a reusable agent. Unlike ChatGPT where conversations disappear, your Threads interactions create agents that can:

• Remember their training across sessions
• Be deployed as production code through Hypermode’s infrastructure
• Work with your specific tools and data through Connections
• Handle complex, multi-step workflows

Tools, not just text

Agents in Threads don’t just chat—they act. Through Connections, your agents can:

• Access your Slack channels and manage team communications
• Create Linear issues when bugs are found
• Query GitHub repositories and analyze pull requests
• Check your Vercel deployments and build logs
• Search the web for up-to-date information
• Integrate with custom tools through Model Context Protocol (MCP) servers

Natural language to deployable code

Everything you build in Threads can be exported as real software:

• Your agent’s personality and instructions
• The tools it has access to
• Its conversation patterns and responses
• Complete Modus apps through eject to code

Getting started: your first agent

Let’s build a Code Review Agent that helps catch bugs and suggest improvements. This example shows how Threads transforms a simple idea into a working agent.

Step 1: Start with intent

You: "I need an agent that reviews pull requests, checks for security issues,
     and suggests better variable names"

Hypermode Concierge: "I'll help you build a Code Review Agent. Let me set up the basic
                      structure and tools..."

The Hypermode Concierge (Hypermode’s agent-building agent) scaffolds your new agent with:

• A focused system prompt
• Access to GitHub tools through Connections
• Code analysis capabilities
• Customized instructions for your workflow

Step 2: Train through conversation

Through iterative dialogue, you refine your agent’s behavior in real-time—each exchange teaches it to better understand your specific requirements and workflow preferences.

You: "Here's a PR with a potential SQL injection vulnerability"
[Pastes code snippet]

Agent: *Analyzes code using GitHub tools*
"I found a SQL injection risk on line 23. The user input isn't sanitized
before being passed to the query. I'd recommend using parameterized queries."

You: "Good catch! But also check variable naming conventions"

Agent: *Updates behavior based on your feedback*
"You're right. I also notice variables like 'data1' and 'temp_thing'
should be more descriptive. I'll include naming suggestions in future reviews."

You: "Perfect. Now I want to deploy this agent"

Step 3: Deploy to production

When your agent works exactly how you want:

You: "I want to deploy this agent to handle our GitHub PRs automatically"

Threads: *Agent is now available for deployment through Hypermode*
- Agent configuration and prompts saved
- Tool integrations and permissions configured
- Ready for deployment
- Accessible through Hypermode's infrastructure

Your conversation just became a deployable agent.

Step 4: Iterate and enhance

Your agent is working, but now you want to streamline your team’s workflow. Through continued conversation, you can enhance your agent’s capabilities in real-time.

You: "When you find issues in PRs, can you create Linear tickets automatically?
     That would save us a lot of manual work."

Agent: "Absolutely! I can integrate with Linear to create tickets for issues I find.
        I'll need access to your Linear workspace. What priority should I set for
        security issues vs. code quality issues?"

You: "Security issues should be High priority, code quality Medium.
     Tag them with 'code-review-bot' so we know the source."

Agent: "Perfect! I'm now connecting to Linear and updating my workflow:
        • Security vulnerabilities → High priority Linear tickets
        • Code quality issues → Medium priority tickets
        • All tickets tagged with 'code-review-bot'
        Ready to test! Try submitting a PR with issues."

Now your agent has evolved into a fully automated workflow that not only reviews code but takes action on what it finds:

Your enhanced agent now:

• Analyzes every new pull request automatically
• Detects security vulnerabilities and code quality issues
• Creates prioritized Linear tickets with appropriate tags
• Comments on PRs with detailed feedback and ticket links
• Maintains context about your team’s priorities and conventions

What changed: through a simple 3-message conversation, your agent learned to integrate with Linear, understand your priority system, and automate ticket creation. No configuration files, no API documentation—just natural language instruction.

The result: your team’s code review process is now fully automated, from detection to ticket creation, saving hours of manual work while ensuring nothing falls through the cracks.

Advanced patterns

Agent specialization

Agents can be built for specific domains with tailored tool access:

GitHub Agent → Specialized for code review and repository management
     ↓
Slack Agent → Focused on team communication and coordination
     ↓
Vercel Agent → Optimized for deployment analysis and infrastructure

Each agent excels in its domain while maintaining consistent behavior patterns.

Task automation

Turn any conversation into automated workflows:

You: "Create a task that analyzes our weekly GitHub activity"

Threads: *Generates structured task definition*
- Captures conversation context
- Defines repeatable workflow
- Maintains agent configuration

Context persistence

Agents remember training across sessions:

Agent: "Based on our previous conversations, I remember you prefer detailed
        code reviews with security focus. Should I continue with that approach?"

You: "Yes, but also add performance considerations"

Agent: "I'll update my review criteria to include performance analysis
        alongside security and code quality."

Model flexibility

Threads works with multiple models through Hypermode’s Model Router:

• Generative Pre-trained Transformer 4.1: Latest OpenAI model for complex reasoning and code analysis
• Claude 4: Anthropic’s advanced model for writing and analysis tasks
• Gemini: Google’s model for multi-modal tasks and data processing
• O1/O3: OpenAI’s reasoning models for complex problem-solving
• Open source models: Llama, Mistral, and other community models

Switch models to optimize for different tasks:

You: "Use Claude for the writing tasks and GPT-4.1 for code analysis"

Agent: *Adapts model usage based on task type*
"I'm now using Claude for documentation and GPT-4.1 for technical analysis.
The combination provides better results for both areas."

Security and governance

Tool access control

Agents only get the tools they need:

• GitHub Agent: Repository access only (no deployment permissions)
• Slack Agent: Communication tools only (no code access)
• Vercel Agent: Deployment analysis only (no user data access)

Team collaboration

• Domain experts build and train agents through conversation
• Platform teams manage deployment and infrastructure
• Security teams audit tool permissions and data access

Full observability

Every agent action is traceable:

• Tool calls and responses logged
• Message history preserved
• Agent behavior patterns tracked
• Performance metrics collected

Best practices

Start specific

❌ "Build me a general assistant"
✅ "Build an agent that analyzes our GitHub PRs and creates Linear issues for bugs"

Train iteratively

❌ "Here's a 500-line requirements document"
✅ "Let's start with basic PR reviews and add complexity through conversation"

Deploy early

❌ "Perfect every detail before deploying"
✅ "Deploy working versions and iterate based on real usage"

Document context

❌ "Figure out what I mean"
✅ "When I say 'urgent', treat it as high priority and notify the team lead"

Get started today

Ready to build your first agent? Here’s how:

1. Sign up for Hypermode and access Threads
2. Start a conversation with the Hypermode Concierge about what you want to build
3. Train your agent through natural language iteration
4. Configure tool access through Connections
5. Deploy and monitor with Hypermode’s infrastructure

Threads transforms how you think about AI—from disposable conversations to persistent, capable agents that integrate seamlessly with your existing tools and workflows.

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Ready to turn your ideas into agents? Start building in Threads →