Two Modes of Iteration
Building Iteration — Expanding Your Agent
After your MVP agent or sample agent runs, you’ll want to expand its capabilities. This is like debugging, but forward-looking: instead of fixing what’s broken, you’re adding what’s missing. Common iteration patterns:- Add new integrations — Connect your agent to additional tools and APIs as your workflow expands
- Handle more edge cases — Add nodes and edges that cover scenarios your initial agent didn’t address
- Improve prompts — Refine node prompts based on real-world output quality
- Add human-in-the-loop gates — Insert approval steps for high-risk actions you initially automated
- Tighten constraints — Add cost limits, timeout policies, or quality thresholds
Production Iteration — Evolution and Adaptation
Once your agent is deployed, your taste and judgment still drive the direction — but AI becomes a significant force multiplier for rapidly iterating and solving problems. With Aden Cloud, production evolution can be fully automatic. The system runs continuous evaluation cycles:Version Control
Iteration doesn’t always improve everything. Sometimes a change helps one use case but hurts another. Hive includes version control so you can always go back.Agent Personality
Not everything should change with iteration. Your agent has a personality — the tone, values, and goals that define who it is. Success isn’t about having your agent constantly changing. It’s about knowing that your goal and personality stay fixed while the agent adapts to solve problems. When you set up your agent’s goal, the core identity persists across evolution cycles. The framework changes how the agent achieves the goal, not what the goal is.Memory Management
As your agent iterates and runs more sessions, memory management becomes important. Hive nodes have built-in mechanisms for:- Node memory — Each node maintains its own working memory within a session
- Memory passing — Structured data flows between nodes through edges
- Cross-session memory — Persistent memory that survives between runs using memory tools
- Memory compaction — Automatic cleanup to prevent unbounded memory growth
The Iteration Lifecycle
Over time, your agent should progress through these stages:| Stage | What You Do | What Hive Does |
|---|---|---|
| MVP | Build and test with templates | Generate graph, validate structure |
| First Success | Deploy and monitor | Run, log, and surface attention flags |
| Expansion | Add integrations and edge cases | Track what works and what fails |
| Maturity | Set it and monitor | Evolve automatically based on outcomes |
Everything before “First Success” should run as smoothly as possible — this is non-negotiable. The docs, templates, and tools are all designed to get you there fast.
Contributing Your Improvements
If you’ve built something useful or found a better way to solve a common problem, share it with the community:- Open an issue on GitHub if you encounter limitations
- Share your agent in Discord for feedback
- Contribute a template if your agent solves a common business process — see CONTRIBUTING.md