Planner mode

Planner agents represent a groundbreaking advancement in AI technology, offering an unprecedented level of task orchestration and multi-agent coordination. These agents stand at the forefront of Agentic AI, showcasing the remarkable ability to plan, reason, execute, and review complex tasks by leveraging a network of specialised agents constantly reviewing and adjusting their strategies based on both internal and external feedback.

Key Features

1. Dynamic Multi-Agent Orchestration: Planner agents can coordinate and utilise multiple specialised agents to execute complex, multi-step tasks. This feature represents a quantum leap in AI capabilities, allowing for the seamless integration of diverse AI skills and knowledge bases.

2. Autonomous Planning and Execution: These agents can independently develop, adjust, and execute sophisticated plans, marking a significant step towards truly autonomous AI systems.

3. Adaptive Task Management: Planner agents can dynamically adjust their strategies based on task progress and outcomes, showcasing an impressive level of adaptability and problem-solving capability.

4. Cross-Functional Integration: By leveraging various agent types (Retriever, Coder, Casual and Desktop), Planner agents can tackle complex, multi-disciplinary challenges that would be impossible for a single AI agent and scripted prompt chaining.

5. Dynamic Agent Generation: In scenarios where existing agents are insufficient, Planner agents can generate new, specialised agents on the fly to meet specific task requirements.

6. Approval and Review Mechanisms: Built-in approval processes and plan adjustment capabilities ensure human oversight and control over complex AI-driven operations.

7. Extensive Execution Capabilities: With a maximum runtime of 60 minutes and the ability to execute up to 20 steps, Planner agents can handle lengthy, complex tasks autonomously.

Planner Tools and Settings

• Available agents for planning: Select the agents that will be used to execute tasks. In case no agents are selected, the planner will attempt to use dynamically generated agents. If not even the Dynamic agents generation is enabled then the planner will return an error.
• Planning instructions: Provide custom instructions for better domain understanding and planning. This can include specific goals, constraints, or preferences in terms of which agents to use for which task or in-context examples of possible plans to tackle a given task.
• Review instructions: Provide custom instructions for the planner agent, whether it is a custom Reviewer or not, to evaluate each step of the plan and ensure it meets the desired criteria. See more on Reviewer provider and Reviewer model here
• Max execution steps: Set the maximum number of steps the planner can execute (up to 20).
• Max re-attempts: Define how many times the planner can re-attempt a failed task. This setting applies to both step failures and code execution failures.
• Approval before execution: Toggle requirement for human approval before task executions. Whether a task requires approval or not can be included in the planning instructions.
• Allow plan adjustment: Enable the planner to adjust the plan if a task is deemed unexecutable. This will be attempted only once for any given plan execution to avoid infinite looping.
• Dynamic agents generation: Allow the planner to create new agents as needed for task execution. At the end of the plan execution, the agents are automatically deleted.
• Email notifications: Options for email notifications on approval requests, task completion, or failures.

Agentic AI

The Planner agent's ability to orchestrate multiple agents in a dynamic planning, reasoning, execution, and review process is truly revolutionary. This "Agentic Feature" represents a paradigm shift in AI capabilities:

1. Unprecedented Problem-Solving: By coordinating multiple specialised agents, Planner agents can tackle complex, multi-faceted problems that were previously beyond the scope of single-function AI.

2. Adaptive Intelligence: The ability to dynamically adjust plans and even create new agents showcases a level of adaptability that closely mimics human problem-solving processes.

3. Autonomous Decision-Making: While maintaining human oversight, Planner agents exhibit a remarkable degree of autonomy in planning and executing complex tasks.

4. Scalable AI Solutions: This feature allows for the creation of highly scalable AI solutions that can grow in complexity and capability as needed.

5. Cross-Domain Integration: By leveraging diverse agent types, Planner agents can integrate knowledge and capabilities across multiple domains, leading to more comprehensive and innovative solutions.

6. Enhanced Efficiency: The ability to coordinate multiple agents simultaneously can significantly reduce the time and resources required for complex task completion.

7. Continuous Learning and Improvement: Through the planning, execution, and review process, these agents have the potential to continuously refine their strategies and improve their performance over time.

Use Cases

1. Complex Project Management:

   • Coordinating various aspects of large-scale projects across different domains.
   • Example: Managing the development of a new software product, including coding, design, marketing, and customer feedback integration.

2. Advanced Research and Analysis:

   • Conducting comprehensive, multi-faceted research by leveraging various knowledge sources and analytical tools.
   • Example: Analysing market trends, competitor strategies, and consumer behaviour to develop a new product launch strategy.

3. Automated Workflow Optimisation:

   • Analysing and optimising complex business processes across different departments.
   • Example: Streamlining a company's supply chain by coordinating inventory management, logistics, and customer demand forecasting.

4. Cross-Functional Problem Solving:

   • Addressing issues that span multiple disciplines, such as technical, financial, and operational challenges.
   • Example: Resolving a complex customer service issue that involves technical support, billing, and product development teams.

5. Innovative Product Development:

   • Coordinating design, engineering, and market analysis for new product creation.
   • Example: Developing a new smart home device by integrating hardware design, software development, user experience research, and market positioning.

6. Comprehensive Data Analysis and Reporting:

   • Gathering, analysing, and presenting data from multiple sources in a coherent and insightful manner.
   • Example: Creating a detailed annual report for a multinational corporation, including financial analysis, market performance, and future projections.

7. Automated Customer Service Orchestration:

   • Managing complex customer inquiries that require input from multiple departments or knowledge bases.
   • Example: Handling a complex insurance claim that involves policy verification, damage assessment, and payout calculation.

8. Regulatory Compliance Management:

   • Ensuring adherence to complex regulatory requirements across various business operations.
   • Example: Coordinating compliance checks across finance, HR, and operations departments for a new international business venture.

9. Strategic Business Planning:

   • Developing comprehensive business strategies by analysing market conditions, internal capabilities, and future trends.
   • Example: Creating a five-year expansion plan for a retail chain, considering location analysis, market trends, and financial projections.

10. Crisis Management and Response:

    • Coordinating rapid, multi-faceted responses to complex crisis situations.
    • Example: Managing a company's response to a cybersecurity breach, including technical mitigation, public relations, and legal compliance aspects.

Limitations and Considerations

• Planner agents models selection is limited due to the complex reasoning required.
• They cannot perform certain operations like complex PowerPoint creation, PDF conversion, image extraction from documents, or audio file creation.
• The maximum autonomous runtime is limited to 60 minutes for security reasons. Enterprise customers can ask for the extension of the runtime.
• While highly capable, Planner agents still require human oversight and may not fully understand nuanced or context-dependent aspects of complex tasks.
• The effectiveness of Planner agents depends on the quality and capabilities of the specialised agents they can access and the environment access and awareness in general.
• There may be a learning curve for users to effectively utilise the full potential of Planner agents, particularly in setting up optimal planning and review instructions. Stay calm and keep prompting!

Best Practices for Utilising Planner Agents

1. Clear Goal Definition: Clearly define the overall objective and desired outcomes for the Planner agent to ensure focused and effective planning.

2. Detailed Task Breakdown: Provide a comprehensive breakdown of the task components to help the Planner agent create more accurate and efficient plans.

3. Customise Planning Instructions: Tailor the planning instructions to your specific domain or industry for more relevant and effective task execution.

4. Regular Monitoring and Feedback: While Planner agents are autonomous, regular monitoring and feedback can help refine their performance over time.

5. Leverage Human Expertise: Use the approval and review mechanisms to incorporate human expertise and judgement, especially for critical decisions.