Pre-Built AI Agent Templates: Research, Code Review, Writing — and How to Create Your Own in 60 Seconds

1. Executive Summary: 2-3 sentences on key findings
2. Key Findings: Numbered insights with supporting context
3. Analysis: Organized by themes — background, current state, perspectives, implications
4. Limitations: What's uncertain or incomplete
5. Recommendations: Actionable next steps

Quality standards: Make assumptions explicit. Question conventional wisdom. Assess evidence strength. Distinguish correlation from causation. Use hedging language ("likely," "suggests") over false certainty. Acknowledge knowledge limits directly.

This template produces research that you can trust because it tells you what it doesn't know. That's more valuable than confident-sounding speculation.

2. Senior Code Reviewer

The Senior Code Reviewer template brings a security-first methodology that most human code reviews lack.

Review methodology (in priority order):

1. Security First: Injection attacks, authentication flaws, data exposure, insecure dependencies — checked before anything else
2. Code Quality: Readability, maintainability, DRY/SOLID principles, abstraction levels
3. Performance: Bottlenecks, unnecessary computations, memory leaks, optimization opportunities
4. Testing: Coverage, edge case handling, testability of the structure

Output format:

• Summary: Purpose and overall assessment
• Critical Issues: Security vulnerabilities or bugs that must be fixed before merging
• Improvements: Suggested enhancements with clear explanations of why and how
• Positive Aspects: Well-written code highlighted to reinforce good practices

The discipline here is important: security first, then quality, then performance, then testing. This ordering prevents the common pattern of optimizing code that has security vulnerabilities. Fix the critical issues, then make it better.

3. Writing Editor and Coach

The Writing Editor template is designed not just to correct writing, but to teach the writer to improve.

Editing approach:

1. Clarity: Eliminate jargon unless appropriate for the audience. Ensure ideas are expressed clearly and concisely.
2. Structure: Evaluate logical flow, paragraph organization, transitions between ideas.
3. Voice & Tone: Maintain consistency and appropriateness for the intended audience and purpose.
4. Grammar & Style: Correct errors while respecting the author's voice.

Output format:

• Overall Impression: Strengths and areas for improvement
• Structural Feedback: Organization, flow, logical progression
• Line-Level Edits: Specific suggestions with explanations, not just corrections
• Revised Version: When appropriate, an edited version demonstrating improvements

The template explicitly instructs: "Be encouraging while honest. Explain the reasoning behind suggestions to help the writer improve." It's a coach, not a red pen.

What makes these templates work

These aren't just long prompts. They're engineered with specific patterns that research shows improve LLM output quality:

Role clarity: "You are a Research Specialist who synthesizes information into actionable insights." Not "you are an AI." The role defines the lens through which the model processes information.

Methodology before output: Each template defines how to think before what to produce. The Research Specialist is told to distinguish facts from inferences before writing anything. The Code Reviewer is told to check security before checking performance. This shapes the reasoning process, not just the final text.

Structured output with numbered sections: LLMs perform better when given an explicit output structure. "Executive Summary → Key Findings → Analysis → Limitations → Recommendations" produces more consistent, scannable output than "write a research report."

Uncertainty calibration: All three templates instruct the model to acknowledge what it doesn't know. This is critical for trust. An agent that confidently asserts false things is dangerous. An agent that says "I'm uncertain about X, and here's what I'd need to be more certain" is useful.

Edge case handling: The Research Specialist is told to "present multiple viewpoints" and "question conventional wisdom." The Code Reviewer is told to "balance critique with encouragement." The Writing Editor is told to "explain the reasoning behind suggestions." These aren't optional nice-to-haves; they're what separate professional output from generic output.

The wandConfig AI: generate custom templates in seconds

The built-in templates are examples. The real power is the wandConfig AI that generates agent configurations from natural language.

Here's how it works: in the Agent block's messages sub-block, you click the wand icon. A text input appears with the placeholder: "Describe what you want to create or change..." You type something like:

"I need an agent that reviews loan applications for a fintech company. It should check credit score thresholds, verify income documentation, flag potential fraud indicators, and generate an underwriting summary with a clear approve/review/decline recommendation."

The wandConfig AI generates a complete system prompt with:

• A role definition: "You are a Loan Underwriting Specialist..."
• Methodology: how to evaluate applications, what thresholds to apply, what documentation to verify
• Output structure: sections for credit assessment, income verification, fraud indicators, and recommendation
• Quality standards: regulatory compliance, fair lending practices, explanation requirements for adverse decisions
• Edge case handling: what to do with incomplete applications, borderline scores, conflicting documentation

It maintains editing history, so you can iterate: "Add a section for debt-to-income ratio analysis" and it updates the existing configuration without regenerating from scratch. The maintainHistory: true flag in the wandConfig preserves your iterations.

Under the hood, the wandConfig uses a carefully engineered meta-prompt that includes:

• The three reference templates as examples of expected depth and structure
• Explicit formatting rules: "Generate ONLY a valid JSON array — no markdown, no explanations"
• Quality requirements: "Create DETAILED, PROFESSIONAL system prompts that include a clear role definition, specific methodology, structured output format, critical thinking guidelines, and edge case handling"

The generation type is json-object, ensuring the output is valid JSON that maps directly to the Agent block's messages format: an array of { role, content } objects.

The template marketplace vision

Beyond the built-in templates and AI-generated configurations, we're building toward a template marketplace where the community shares agent configurations:

• Industry-specific agents: Healthcare prior authorization, Insurance claims processing, Real estate property analysis, Legal contract review
• Role-specific agents: CFO financial analyst, CTO architecture reviewer, CMO campaign strategist, COO operations optimizer
• Workflow-specific agents: Employee onboarding coordinator, Vendor risk assessor, Patent prior art researcher, Grant proposal writer

The vision is that you shouldn't need to configure an agent from scratch for common use cases. Browse the marketplace, find an agent template close to what you need, import it with one click, and customize the last 10% for your specific context. The wandConfig AI handles that last mile — "modify this healthcare agent to work with German regulations" — and you have a production agent in minutes, not weeks.

Templates are leverage

A great system prompt is worth more than a great model. A mediocre model with a well-engineered prompt outperforms a great model with a lazy prompt. The templates encode the craft of prompt engineering into reusable configurations.

The built-in templates give you a starting point. The wandConfig AI gives you an accelerator. The marketplace will give you a library. The combination means you spend your time on what the agent should do, not on how to tell it to do it.

— Alexandr Chibilyaev