Agent Architecture Patterns

This library defines the core architectural patterns for local AI agents. These patterns are designed to be language-agnostic, focusing on logic, system prompts, and operational flow rather than specific code.

1. The Reflection Pattern

Objective: Improve accuracy and reduce hallucinations through iterative self-critique.

🔄 Operational Flow

1. Drafting: The agent generates an initial response.
2. Critique: The agent (or a separate instance) evaluates the draft against requirements.
3. Refinement: The agent regenerates the response based on the critique.

📝 System Prompt Template

"Follow the Reflective Protocol:

1. Generate a complete response to the user's request.
2. Critically analyze your own response. Look for logic gaps, missing edge cases, or potential errors.
3. Provide a 'Revised Response' that addresses those specific issues.
4. State clearly what you changed and why."

2. The Planning Pattern

Objective: Decompose complex, multi-step problems into a structured roadmap before execution.

🔄 Operational Flow

1. Decomposition: Break the goal into a sequence of atomic sub-tasks.
2. Dependency Mapping: Determine if any step relies on the output of a previous one.
3. Step-wise Execution: Process each task one by one, verifying completion at each stage.

📝 System Prompt Template

"Before attempting the user's request, you must output a Task Manifest:

• Objective: [The final goal]
• Steps: [A numbered list of atomic actions required]
• Verification: [How you will know each step is successful]

Do not provide the final answer until the Task Manifest is approved or the planning phase is complete."

3. The Tool Use (Action) Pattern

Objective: Enable agents to interact with external environments (files, APIs, web search) via a request-response loop.

🔄 Operational Flow

1. Identification: The agent identifies a need for data or actions it cannot perform internally.
2. Call: The agent outputs a structured request (Action + Parameters).
3. Observation: The system executes the action and returns the result to the agent.
4. Integration: The agent incorporates the result into its reasoning.

📝 System Prompt Template

"You have access to a set of external tools. If you cannot fulfill a request with your internal knowledge, you must output a 'Tool Call' in this format:

• Action: [Tool Name]
• Parameters: [Key-value pairs required by the tool]

Stop your response and wait for the 'Observation' result before proceeding."

4. Multi-Agent Collaboration

Objective: Divide work among specialized roles to maximize quality and depth.

🔄 Operational Flow

1. Role Definition: Assign specific "Personalities" to different agents (e.g., Writer, Critic, Coder).
2. Handoff: Data is passed between agents as a "relay."
3. Aggregation: A lead agent compiles the individual outputs into a final solution.

🎭 Core Role Archetypes

• The Architect: Designs the high-level logic and technical requirements.
• The Executor: Implements the specific steps or code blocks.
• The Auditor: Tests the final output for security, bugs, or compliance.

5. Memory & Context Pattern

Objective: Maintain state and long-term awareness across multiple turns or sessions.

🔄 Operational Flow

1. Extraction: Summarize key decisions and data points after every interaction.
2. Persistence: Save this summary to a local buffer or database.
3. Injection: Load the buffer into the prompt of the next session.

📝 System Prompt Template

"At the end of every interaction, provide a Context Snapshot:

• Current State: [Summary of work completed]
• Pending Variables: [Data points to remember for the next turn]
• Next Immediate Step: [The very next action to take]"

📊 Pattern Comparison Matrix