Agentic Workflows
Build autonomous AI agents that reason, plan, and execute complex multi-step tasks using MyBotBox's agentic workflow blocks.
Agentic workflows go beyond simple prompt-response patterns. They enable AI agents to reason about problems, plan multi-step solutions, use tools autonomously, and learn from their actions -- all within the visual MyBotBox canvas.
What Makes a Workflow "Agentic"?
Traditional workflows follow a fixed sequence: Input -> Process -> Output. Agentic workflows are different -- the AI dynamically decides what to do next based on context, goals, and intermediate results.
An agentic workflow gives the AI autonomy to choose its path, use tools, reflect on results, and adapt its approach -- while you maintain control through guardrails and human-in-the-loop checkpoints.
Key characteristics of agentic workflows:
- Goal-driven: The agent works toward an objective rather than following rigid steps
- Tool use: The agent decides which tools to call and when
- Self-reflection: The agent evaluates its own outputs and corrects mistakes
- Memory: The agent remembers context across interactions
- Planning: The agent breaks complex tasks into subtasks
Core Agentic Blocks
MyBotBox provides purpose-built blocks for each agentic capability:
Reasoning and Planning
Agent
The foundational AI block. Connects to any LLM provider, processes instructions, and generates responses with tool-calling support.
ReAct Loop
Implements the Reason-Act-Observe loop. The agent reasons about what to do, takes an action, observes the result, and repeats until the task is complete.
Goal Planner
Breaks high-level goals into actionable sub-tasks with dependency tracking and progress monitoring.
Goal Setter
Defines objectives and success criteria that guide the agent's decision-making throughout the workflow.
Plan with Backtracking
Creates execution plans that can backtrack and try alternative approaches when a step fails.
Memory and Learning
Episodic Memory
Stores and retrieves memories of past interactions, enabling the agent to learn from experience.
Persistent Memory
Long-term memory that persists across workflow executions, building institutional knowledge over time.
Cross-Agent Learning
Share learnings between multiple agents so improvements in one agent benefit others.
Self-Reflection
The agent evaluates its own outputs against quality criteria and iterates to improve.
Autonomy and Control
Self-Modification
Allows the agent to modify its own instructions or parameters based on performance feedback.
Guardrails
Safety boundaries that constrain agent behavior -- content filtering, output validation, and policy enforcement.
Human in the Loop
Pause execution for human review and approval at critical decision points.
Evaluator
Score and assess agent outputs against defined rubrics for quality assurance.
Multi-Agent Coordination
Multi-Agent Team
Orchestrate multiple specialized agents working together on a shared task with role assignment and communication.
Research Agent
A pre-configured agent pattern optimized for information gathering, synthesis, and report generation.
Persistent 24/7 Agent
Long-running agents that operate continuously, monitoring events and responding autonomously.
Dynamic Workflow Generator
Agents that can create and execute new workflows on-the-fly based on the task at hand.
Building Your First Agentic Workflow
Define the goal: Start with a Goal Setter block to establish what the agent should accomplish and how to measure success.
Add reasoning: Connect a ReAct Loop or Goal Planner block to give the agent the ability to plan and reason about its approach.
Equip with tools: Add tool blocks (API, Knowledge, Function) that the agent can call to gather information and take actions.
Add safety: Place Guardrails and Human in the Loop blocks to maintain control over the agent's behavior.
Enable learning: Connect Episodic Memory or Self-Reflection blocks so the agent improves over time.
Common Agentic Patterns
Research and Report Generation
Combine Research Agent + Knowledge + Evaluator to create an agent that autonomously gathers information from multiple sources, synthesizes findings, and produces a quality-checked report.
Customer Support Agent
Use Agent + ReAct Loop + Guardrails + Persistent Memory to build a support agent that reasons about customer issues, looks up relevant documentation, remembers past interactions, and stays within policy guidelines.
Data Pipeline with Quality Control
Chain Goal Planner + API blocks + Self-Reflection + Human in the Loop to create a data processing pipeline where the agent plans the extraction strategy, executes it, self-evaluates the results, and escalates edge cases to humans.
Autonomous Task Manager
Deploy a Persistent 24/7 Agent + Goal Planner + Cross-Agent Learning to create an always-on agent that monitors incoming tasks, delegates to specialized sub-agents, and continuously improves its task routing based on outcomes.
Best Practices
-
Start simple, add complexity gradually: Begin with an Agent + ReAct Loop, then layer on memory and planning as needed.
-
Always include guardrails: Agentic workflows have more autonomy, so safety boundaries are essential. Set token limits, content filters, and approval gates.
-
Use structured outputs: Configure your Agent blocks to return JSON schemas. This makes it easier for downstream blocks to process agent decisions reliably.
-
Monitor and evaluate: Connect Evaluator blocks to track agent performance metrics. Use these insights to tune instructions and improve outcomes.
-
Leverage memory strategically: Not every workflow needs memory. Use Episodic Memory for conversational agents and Persistent Memory for agents that need to learn across sessions.
Next Steps
- Agent Block Reference -- Configure LLM providers, tools, and structured outputs
- ReAct Loop -- Implement reasoning-action loops
- Guardrails -- Set safety boundaries for autonomous agents
- Execution Basics -- Understand how workflows execute