M365 Agents TS

M365 Agents Ts

Orchestrates intelligent skill selection and execution for m365 agents ts workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

import { GraphClient, AgentCapability, SkillMetadata } from '@microsoft/m365-agents-sdk';

export async function routeM365AgentSkill(
  userContext: { tenantId: string; userId: string; intent: string },
  availableAgents: AgentCapability[],
  minConfidence: number = 0.75
): Promise<SkillMetadata | null> {
  // Law 1: Early exit on invalid boundary input
  if (!userContext.intent?.trim()) {
    throw new Error("Intent extraction failed: empty user context");
  }

  const graphClient = new GraphClient(userContext.tenantId);
  const userPermissions = await graphClient.getPermissions(userContext.userId);
  
  let bestMatch: SkillMetadata | null = null;
  let highestScore = 0;

  for (const agent of availableAgents) {
    // Law 2: Parse & validate state before scoring
    const intentMatch = calculateSemanticRelevance(userContext.intent, agent.triggers);
    const permissionMatch = userPermissions.includes(agent.requiredPermission) ? 1.0 : 0.0;
    const healthScore = await graphClient.getAgentHealth(agent.id);
    
    // Multi-factor scoring: intent relevance, tenant permissions, service health
    const compositeScore = (intentMatch * 0.5) + (permissionMatch * 0.3) + (healthScore * 0.2);
    
    if (compositeScore > highestScore && compositeScore >= minConfidence) {
      highestScore = compositeScore;
      // Law 3: Return new object, never mutate original capability metadata
      bestMatch = { ...agent, confidence: compositeScore, selectedAt: new Date().toISOString() };
    }
  }

  if (!bestMatch) {
    return null;
  }

  return bestMatch;
}

Pattern 2: Execution with Fallback

import { GraphAPIError, RetryPolicy, FallbackChain, ExecutionResult } from '@microsoft/m365-agents-sdk';

export async function executeM365AgentTask(
  skill: SkillMetadata,
  taskPayload: Record<string, unknown>,
  fallbackChain: FallbackChain
): Promise<ExecutionResult> {
  // Law 2: Validate payload against M365 Graph schema before execution
  const validatedPayload = validateM365Payload(taskPayload, skill.schema);
  const maxRetries = 2;
  const attemptStart = Date.now();
  
  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      // Law 4: Fail fast on invalid state, don't patch bad data
      const result = await skill.handler(validatedPayload);
      return {
        success: true,
        skillId: skill.id,
        data: result,
        attempts: attempt + 1,
        latencyMs: Date.now() - attemptStart,
        confidence: skill.confidence
      };
    } catch (error) {
      if (error instanceof GraphAPIError && error.status === 429) {
        // Transient rate limit - exponential backoff per M365 Graph guidelines
        await RetryPolicy.exponentialBackoff(attempt, 1000, 5000);
        continue;
      }
      if (error instanceof GraphAPIError && error.status === 403) {
        // Law 4: Hard failure on permission denial
        throw new Error(`Permission denied for ${skill.id}: ${error.message}`);
      }
      if (attempt === maxRetries) {
        // Fallback chain: try alternative M365 service or defer
        const fallbackResult = await fallbackChain.execute(skill.id, validatedPayload);
        if (fallbackResult.success) return fallbackResult;
      }
    }
  }
  
  // Law 4: Fail loud after exhaustion
  throw new Error(`M365 Agent ${skill.id} failed after ${maxRetries + 1} attempts and fallback exhaustion`);
}

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

| Skill | Purpose | |

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Microsoft Graph TypeScript SDK
• Power Platform JavaScript API Reference
• Node.js M365 Integration Guide
• Teams App Development with Node.js
• M365 Copilot Plugin Development