The pillars of modern prompt engineering in 2026
In 2026, the way we design prompts for large language models (LLMs) has changed dramatically. The most significant developments come from three fronts: Anthropic’s runtime sandbox, which protects agents at the operating system level; the Saluki 27B tool-calling model, which brings advanced reasoning capabilities; and Cognition, which provides agent-safety plugins. Mastering these technologies means knowing how to combine structured prompts, security constraints, and performance optimisation.
1. Understanding Anthropic’s runtime sandbox
Anthropic’s runtime sandbox (srt) is an open-source environment that isolates agents and MCP servers, limiting access to the file system and network. To leverage this protection in prompts, you must explicitly declare the actions that are permitted.
# Example prompt for Claude Code with sandbox constraints
You are an AI assistant with the following capabilities:
- Read files in /tmp/analysis/
- Execute Python scripts that stay within the sandbox
- Make HTTP requests to internal APIs only
When the user asks for a task, follow these steps:
1. Verify the requested file exists in /tmp/analysis/
2. Run the analysis script safely
3. Return the result without exposing system details.This approach keeps the agent responsive while respecting the sandbox’s boundaries.
2. Integrating Saluki 27B’s tool-calling abilities
Saluki 27B is a model optimised for tool calling, achieving excellent memory efficiency (around 12 GB) while retaining a high degree of reasoning. To get the best performance, prompts should include a clear description of the available tools and the desired output format.
# Prompt for Saluki 27B focused on problem solving
Goal: Identify and fix the reported bug in the Node.js service.
Available tools:
- list_files(path)
- read_file(path)
- execute_command(cmd)
Please follow this format:
{
"analysis": "Brief explanation of the issue",
"steps": [
{"action": "list_files", "params": {"/var/log"}},
{"action": "read_file", "params": {"/var/log/error.log"}},
{"action": "execute_command", "params": {"cmd": "npm test"}}
],
"fix": "Proposed code change"
}Structuring the prompt in this way guides Saluki 27B toward correct tool use and produces repeatable results.
3. Leveraging Cognition for agent security
Cognition has open-sourced the Devin Vulnerability Fixes plugin, which adds three MIT-licensed capabilities: dependency inventory, patching, and security testing for PHP, Node.js, and GitLab. By inserting these capabilities into prompts, you can automate LLM-based security checks.
# Prompt using Cognition’s capabilities
You are a security agent powered by Cognition’s Devin plugin.
Your tasks:
1. Scan the project for outdated packages (npm list --all).
2. Apply patches using the “apply_patch” skill for each vulnerable package.
3. Run the test suite to confirm fixes.
Output format:
{
"vulnerabilities_found": [...],
"patches_applied": [...],
"tests_passed": true/false
}This integration combines semantic analysis with concrete checks, reducing the risk of security errors.
Advanced techniques: practical examples
Basic structure of a Claude Code prompt
An effective Claude Code prompt consists of four parts: context, goal, constraints, and output format. Use bullet points for constraints and code blocks for examples.
Prompt for vulnerability scanning with Cognition
When you need to audit a repository, combine it with the Devin plugin: the model will interpret the request and use the capabilities to generate a detailed security report.
Optimising parameters for Saluki 27B
Checklist for a successful implementation
- Define clearly the actions permitted by the runtime sandbox.
- Describe the tools available to the Saluki 27B model.
- Incorporate Cognition’s capabilities for automated security checks.
- Test prompts in a controlled environment before deployment.
- Monitor memory usage and latencies to optimise performance.
Conclusion: from simple prompts to sophisticated agents
Advanced prompt engineering in 2026 is no longer just about phrasing sentences; it’s about building agents that are secure, tool-capable, and able to adapt to complex scenarios. By combining Anthropic’s runtime sandbox, Saluki 27B’s tool-calling capabilities, and Cognition’s security plugins, you can create automated workflows that surpass the limits of traditional LLM systems.
Takeaway:Start with a structured prompt that makes context, goal, constraints, and format explicit. Add explicit references to sandbox, tools, and security capabilities. Test, monitor, and iterate for increasingly reliable results.