Agentic engine

The agentic engine is Trame’s AI-powered execution system that converts natural language workflow descriptions into concrete execution plans, manages human approvals, and continuously learns from outcomes. It operates through a structured cycle: plan → approve → execute → learn → improve.

AI Planning Pipeline

The engine follows a sophisticated planning process:

Context Gathering

1. Workflow Analysis: Interprets the natural language workflow description and goals
2. Historical Learning: Incorporates success patterns from previous executions
3. System Integration: Identifies available tools from connected Composio integrations
4. Resource Discovery: Accesses discovered context from successful past runs
5. Configuration Review: Uses workflow-specific settings and required details

Plan Generation

1. Step Decomposition: Breaks down the workflow goal into concrete, executable steps
2. Tool Selection: Maps each step to appropriate integration tools and actions
3. Dependency Analysis: Identifies step dependencies and execution order
4. Resource Requirements: Determines required data, credentials, and permissions
5. Risk Assessment: Evaluates potential failure points and mitigation strategies

Validation and Optimization

1. Capability Check: Verifies all required connectors are healthy and accessible
2. Permission Validation: Confirms adequate OAuth scopes for planned actions
3. Data Completeness: Identifies missing required information before execution
4. Performance Optimization: Optimizes execution order and resource usage

Approvals

• Runs from Draft and Pilot workflows pause after planning and wait for approval.
• Approvers see what the workflow intends to do (steps and tools) before allowing it to proceed.
• Live workflows skip approval and execute immediately when triggered or run manually.

Execution and limits

• Execution follows the approved plan, step by step, and stops if tools are unavailable or inputs are missing.
• Built-in guardrails prevent runaway loops and cap tool retries.
• The engine remembers successful tools and discovered resources so future runs can reuse them.

Learning signals

• Success: Captures what worked so similar runs can reuse patterns.
• Failure: Stores analysis, missing capabilities, and suggested improvements to fix before the next run.
• Rejection: Saves reviewer feedback so future plans avoid the rejected approach.
• Signals stay scoped to your organization and feed into future planning automatically.

Safety notes

• Approval is required for draft/pilot runs, keeping humans in the loop until you promote to Live.
• Connectors are revalidated before use; missing or errored toolkits halt execution with clear errors.
• Keep sensitive data in required details and connector scopes instead of free text prompts.