M365 Agents Dotnet

M365 Agents Dotnet

Orchestrates intelligent skill selection and execution for m365 agents dotnet 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

public class M365AgentOrchestrator
{
    public async Task<SkillExecutionPlan> SelectOptimalSkillAsync(
        string userIntent,
        List<M365SkillMetadata> availableSkills,
        double minConfidence = 0.75)
    {
        // Law 1: Early Exit on invalid input
        if (string.IsNullOrWhiteSpace(userIntent))
            throw new ArgumentException("User intent cannot be empty", nameof(userIntent));
        if (availableSkills == null || !availableSkills.Any())
            throw new InvalidOperationException("No M365 skills registered for routing");

        // Law 2: Parse at boundary, make illegal states unrepresentable
        var intentFeatures = ParseM365Intent(userIntent);
        var scoredCandidates = new List<(M365SkillMetadata Skill, double Score)>();

        foreach (var skill in availableSkills)
        {
            if (!await IsM365SkillAvailableAsync(skill)) continue;

            var similarityScore = CalculateSemanticSimilarity(intentFeatures, skill.Triggers);
            var historicalScore = skill.HistoricalSuccessRate * 0.3;
            var availabilityScore = skill.IsHealthy ? 0.2 : 0.0;
            var totalScore = (similarityScore * 0.5) + historicalScore + availabilityScore;

            if (totalScore >= minConfidence)
                scoredCandidates.Add((skill, totalScore));
        }

        if (!scoredCandidates.Any())
            return null;

        // Law 3: Return new structure, never mutate inputs
        var best = scoredCandidates.OrderByDescending(c => c.Score).First();
        return new SkillExecutionPlan
        {
            SelectedSkill = best.Skill,
            Confidence = best.Score,
            SelectionTimestamp = DateTimeOffset.UtcNow,
            RoutingContext = new Dictionary<string, object> { { "intent", intentFeatures.RawText } }
        };
    }

    private IntentFeatures ParseM365Intent(string input) => new IntentFeatures { RawText = input };
    private double CalculateSemanticSimilarity(IntentFeatures features, List<string> triggers) => 0.85;
    private Task<bool> IsM365SkillAvailableAsync(M365SkillMetadata skill) => Task.FromResult(skill.IsHealthy);
}

Pattern 2: Execution with Fallback

public async Task<ExecutionResult> ExecuteM365SkillAsync(
    SkillExecutionPlan plan,
    M365ExecutionContext context,
    int maxRetries = 2)
{
    // Law 1: Guard clause for orchestration contract
    if (plan == null || context == null)
        throw new ArgumentNullException(nameof(plan), "Orchestration plan and context are required");

    // Law 2: Validate M365 context before touching Graph SDK
    var validatedContext = ValidateM365Context(context);
    
    // Fallback chain: Retry -> Alternative Skill -> Human Defer
    var fallbackChain = new List<Func<M365ExecutionContext, Task<ExecutionResult>>>
    {
        async (ctx) => await RetryWithExponentialBackoffAsync(plan.SelectedSkill, ctx),
        async (ctx) => await TryAlternativeM365SkillAsync(plan.SelectedSkill, ctx),
        async (ctx) => await DeferToHumanOperatorAsync(ctx)
    };

    for (int attempt = 0; attempt <= maxRetries; attempt++)
    {
        try
        {
            // Law 4: Fail fast on transient Graph errors, don't patch
            var result = await ExecuteGraphOperationAsync(plan.SelectedSkill, validatedContext);
            
            // Law 3: Return immutable result snapshot
            return new ExecutionResult
            {
                Success = true,
                SkillName = plan.SelectedSkill.Name,
                Data = result,
                Attempts = attempt + 1,
                LatencyMs = Stopwatch.GetElapsedTime().Milliseconds,
                Confidence = plan.Confidence
            };
        }
        catch (GraphServiceException ex) when (ex.StatusCode == HttpStatusCode.TooManyRequests)
        {
            if (attempt == maxRetries)
                return await ApplyFallbackChainAsync(fallbackChain, validatedContext);
        }
        catch (InvalidM365StateError ex)
        {
            // Law 4: Halt immediately on corrupt/invalid M365 state
            throw new SkillExecutionException($"Invalid M365 state for {plan.SelectedSkill.Name}: {ex.Message}", ex);
        }
    }

    throw new SkillExecutionException($"M365 skill {plan.SelectedSkill.Name} exhausted all {maxRetries + 1} execution attempts.");
}

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 365 Agents Documentation
• Microsoft Graph API for M365 Integration
• Power Automate Agent Builder
• Copilot Studio (M365 Agent Platform)
• Bot Framework for Custom M365 Agents (.NET)