The Agentic AI Handbook: Production-Ready Patterns
From: https://www.nibzard.com/agentic-handbook
The transition from experimental AI demos to stable production systems requires more than clever prompting; it demands architectural rigour. Nikola Balić has compiled 113 patterns derived from real-world implementations, providing a shared vocabulary for developers building autonomous and semi-autonomous systems.
“Agentic patterns are the bridge across this gap. Each pattern represents something more than one team has implemented and validated. They’re not theoretical—they’re born from production experience.”
Core Categories of Agentic Design
The handbook organises these patterns into eight functional areas to address the specific challenges of agent reliability and security:
| Category | Focus Area | Key Pattern Example |
|---|---|---|
| Orchestration | Decision making and planning | Plan-Then-Execute |
| Tool Use | Interaction with external APIs/DBs | Code-Over-API |
| Context | Managing memory and token limits | Episodic Memory Retrieval |
| Feedback Loops | Iterative improvement | Reflection Loop |
| UX/Collaboration | Human-in-the-loop dynamics | Spectrum of Control |
| Reliability/Eval | Quality assurance and testing | Workflow Evals |
| Learning | Institutional knowledge building | Skill Library Evolution |
| Security | Safeguards and privacy | Lethal Trifecta Model |
How to Start: Foundational Patterns
For those moving away from “vibe coding” toward structured engineering, Nikola suggests starting with these four pillars:
- Plan-Then-Execute: Separate the reasoning phase from the action phase. This prevents untrusted data from hijacking the agent’s logic mid-stream.[1]
- Inversion of Control: Instead of micromanaging every step, provide the agent with tools and a high-level goal.
- Reflection Loop: Implement a system where the model critiques its own draft against a metric before finalising output.
- Chain-of-Thought Monitoring: Maintain “a finger on the trigger” by observing intermediate reasoning and interrupting if the agent drifts off-course.
“The jump from single-agent to multi-agent systems follows a natural progression. Specialization beats generalization. An agent optimized for code review will outperform a generalist agent asked to review code.”
The “Lethal Trifecta” Security Framework
A critical takeaway for production environments is the security model. To prevent data exfiltration via prompt injection, a system should never simultaneously possess:
- Access to private data.
- Exposure to untrusted content.
- The ability to communicate externally.
By ensuring at least one of these “circles” is missing in any execution path, developers can create a robust safety boundary that does not rely solely on the model’s ability to follow instructions.
This pattern is notably used in Claude Code’s “plan mode” to increase success rates in complex tasks. ↩︎
