Agent Workflow Patterns with Knowledge Base Integration

Proven patterns and strategies for AI agents to effectively leverage the Coditect knowledge base in their workflows.

Table of Contents

1. Core Workflow Patterns
2. Query Strategy Patterns
3. Multi-Agent Collaboration Patterns
4. Learning and Contribution Patterns
5. Performance Optimization
6. Anti-Patterns to Avoid

Core Workflow Patterns

Pattern 1: Context-Before-Code (CBC)

Intent: Always retrieve historical context before implementing new features or fixing bugs.

Implementation:

async function contextBeforeCode(task: Task) {
  // Step 1: Extract searchable components
  const keywords = extractKeywords(task.description);
  const components = identifyComponents(task);
  
  // Step 2: Search for direct matches
  const historicalContext = await kb.search({
    query: keywords.join(' '),
    categories: task.categories,
    contains_solution: true
  });
  
  // Step 3: If no direct match, broaden search
  if (historicalContext.results.length === 0) {
    const patterns = await kb.findPatterns({
      pattern_type: task.type,
      context: components.join(' ')
    });
    
    return planFromPatterns(patterns);
  }
  
  // Step 4: Validate approach against known issues
  const issues = await kb.checkIssues({
    component: components[0],
    include_workarounds: true
  });
  
  return {
    context: historicalContext,
    issues: issues,
    approach: adaptSolution(historicalContext.results[0])
  };
}

Pattern 2: Progressive Enhancement (PE)

Intent: Start with known working solutions and progressively enhance them.

Implementation:

async function progressiveEnhancement(requirement: Requirement) {
  // Start with proven foundation
  const examples = await kb.getContext({
    problem: requirement.description,
    include_examples: true
  });
  
  // Select and implement base
  const base = selectBestExample(examples);
  let implementation = await implementBase(base);
  
  // Test and iterate
  while (!meetsRequirement(implementation, requirement)) {
    const debugging = await kb.search({
      query: `${getError(implementation)} solution`,
      categories: ['debugging']
    });
    
    implementation = applyFix(implementation, debugging);
  }
  
  // Enhance with patterns
  const enhancements = await kb.findPatterns({
    pattern_type: 'implementation',
    context: requirement.advancedFeatures
  });
  
  return enhanceImplementation(implementation, enhancements);
}

Pattern 3: Defensive Implementation (DI)

Intent: Proactively check for known issues and implement defensive measures.

async function defensiveImplementation(component: Component) {
  // Check all known issues first
  const knownIssues = await kb.checkIssues({
    component: component.name,
    include_workarounds: true,
    active_only: false
  });
  
  // Build defensive measures
  const defensiveMeasures = knownIssues.map(issue => ({
    issue: issue.description,
    prevention: issue.workaround || generatePrevention(issue),
    validation: createValidation(issue)
  }));
  
  // Implement with guards
  return implementWithGuards(component, defensiveMeasures);
}

Query Strategy Patterns

Pattern 1: Funnel Search

Intent: Start broad and progressively narrow search scope.

class FunnelSearch {
  async search(problem: string, context: Context) {
    const queries = [
      // Level 1: Broad search
      { 
        query: extractMainConcept(problem),
        min_relevance: 0.6 
      },
      // Level 2: Component-specific
      { 
        query: `${context.component} ${problem}`,
        categories: [context.category],
        min_relevance: 0.7
      },
      // Level 3: Exact error/pattern
      { 
        query: extractErrorSignature(problem),
        contains_solution: true,
        min_relevance: 0.8
      },
      // Level 4: Time-constrained recent
      {
        query: problem,
        time_window_days: 7,
        min_relevance: 0.9
      }
    ];
    
    for (const q of queries) {
      const results = await kb.search(q);
      if (results.results.length > 0) {
        return this.rankResults(results, context);
      }
    }
    
    return null;
  }
}

Pattern 2: Cross-Reference Search

Intent: Validate findings by cross-referencing multiple search angles.

async function crossReferenceSearch(problem: Problem) {
  // Search from multiple angles
  const searches = await Promise.all([
    // Direct problem search
    kb.search({ query: problem.description }),
    
    // Error pattern search
    kb.search({ query: problem.errorMessage, categories: ['debugging'] }),
    
    // Component issues
    kb.checkIssues({ component: problem.component }),
    
    // Similar sessions
    kb.getSimilarSessions({ 
      task_description: problem.context,
      technologies: problem.techStack 
    })
  ]);
  
  // Cross-reference and score
  const crossReferenced = findCommonElements(searches);
  return rankByFrequency(crossReferenced);
}

Pattern 3: Temporal Search

Intent: Leverage time-based relevance for evolving issues.

async function temporalSearch(issue: Issue) {
  const timeWindows = [1, 7, 30, 90]; // days
  const results = [];
  
  for (const window of timeWindows) {
    const windowResults = await kb.search({
      query: issue.description,
      time_window_days: window,
      min_relevance: 0.8 - (window * 0.002) // Lower threshold for older
    });
    
    results.push({
      window,
      results: windowResults,
      relevance: calculateTemporalRelevance(windowResults, window)
    });
  }
  
  return synthesizeTemporalInsights(results);
}

Multi-Agent Collaboration Patterns

Pattern 1: Knowledge Relay

Intent: Agents pass enriched context through a workflow chain.

class KnowledgeRelay {
  private relayContext: RelayContext = {
    originalTask: null,
    knowledgeChain: [],
    decisions: [],
    appliedSolutions: []
  };
  
  async startRelay(task: Task, firstAgent: Agent) {
    this.relayContext.originalTask = task;
    
    // First agent searches and enriches
    const initialContext = await firstAgent.searchKnowledge(task);
    this.relayContext.knowledgeChain.push({
      agent: firstAgent.id,
      findings: initialContext,
      timestamp: Date.now()
    });
    
    return this.relayContext;
  }
  
  async passRelay(fromAgent: Agent, toAgent: Agent, additionalContext?: any) {
    // Receiving agent builds on previous knowledge
    const enrichedSearch = await toAgent.searchWithContext(
      this.relayContext,
      additionalContext
    );
    
    this.relayContext.knowledgeChain.push({
      agent: toAgent.id,
      findings: enrichedSearch,
      timestamp: Date.now()
    });
    
    // Share back to knowledge base
    if (this.isComplete()) {
      await this.documentRelayLearning();
    }
    
    return this.relayContext;
  }
}

Pattern 2: Consensus Building

Intent: Multiple agents verify solutions through independent searches.

class ConsensusBuilder {
  async buildConsensus(problem: Problem, agents: Agent[]) {
    // Each agent independently searches
    const independentSearches = await Promise.all(
      agents.map(agent => 
        agent.searchKnowledge(problem, { independent: true })
      )
    );
    
    // Find overlapping solutions
    const commonSolutions = this.findCommonSolutions(independentSearches);
    
    // Build consensus score
    const consensus = {
      solutions: commonSolutions,
      confidence: this.calculateConfidence(commonSolutions, agents.length),
      dissent: this.identifyDissent(independentSearches)
    };
    
    // High confidence = proceed
    // Low confidence = escalate or research more
    return consensus;
  }
}

Pattern 3: Specialist Consultation

Intent: General agents consult specialist agents for domain-specific knowledge.

class SpecialistConsultation {
  private specialists = {
    'security': SecurityAgent,
    'performance': PerformanceAgent,
    'architecture': ArchitectureAgent,
    'testing': TestingAgent
  };
  
  async consultSpecialists(task: Task, generalContext: Context) {
    const relevantSpecialists = this.identifyRelevantSpecialists(task);
    
    const consultations = await Promise.all(
      relevantSpecialists.map(async (specialistType) => {
        const specialist = this.specialists[specialistType];
        
        // Specialist searches with domain focus
        const specialistContext = await specialist.searchDomainKnowledge({
          task: task,
          generalContext: generalContext,
          domainFocus: specialistType
        });
        
        return {
          domain: specialistType,
          insights: specialistContext,
          recommendations: specialist.generateRecommendations(specialistContext)
        };
      })
    );
    
    return this.synthesizeConsultations(consultations);
  }
}

Learning and Contribution Patterns

Pattern 1: Solution Documentation

Intent: Systematically document successful solutions for future use.

class SolutionDocumenter {
  async documentSolution(problem: Problem, solution: Solution, process: Process) {
    // Analyze what made this solution successful
    const analysis = {
      problemSignature: this.extractProblemSignature(problem),
      solutionPattern: this.extractSolutionPattern(solution),
      keyInsights: this.extractKeyInsights(process),
      reusabilityScore: this.assessReusability(solution)
    };
    
    // Add to knowledge base with rich metadata
    await kb.addLearning({
      problem: problem.description,
      solution: solution.implementation,
      category: this.categorize(problem, solution),
      complexity: this.assessComplexity(solution),
      tags: [
        ...this.extractTags(problem),
        ...this.extractTags(solution),
        ...process.technologiesUsed
      ],
      code_example: solution.code,
      metadata: {
        ...analysis,
        performance_metrics: process.metrics,
        debug_steps: process.debugSteps,
        decision_points: process.decisions
      }
    });
  }
}

Pattern 2: Pattern Evolution

Intent: Track how patterns evolve over time and update knowledge accordingly.

class PatternEvolution {
  async evolvePattern(patternId: string, newVariation: Variation) {
    // Get existing pattern
    const existingPattern = await kb.getPattern(patternId);
    
    // Analyze variation
    const evolution = {
      originalPattern: existingPattern,
      variation: newVariation,
      improvements: this.identifyImprovements(existingPattern, newVariation),
      tradeoffs: this.identifyTradeoffs(existingPattern, newVariation)
    };
    
    // Update pattern in KB
    await kb.updatePattern(patternId, {
      variations: [...existingPattern.variations, newVariation],
      evolutionHistory: [...existingPattern.evolutionHistory, evolution],
      currentBestPractice: this.selectBestPractice([
        existingPattern,
        newVariation
      ])
    });
    
    // Notify agents using old pattern
    await this.notifyPatternUsers(patternId, evolution);
  }
}

Pattern 3: Failure Learning

Intent: Learn from failures to prevent future occurrences.

class FailureLearning {
  async learnFromFailure(failure: Failure) {
    // Search for similar past failures
    const similarFailures = await kb.search({
      query: failure.errorSignature,
      categories: ['debugging', 'failures']
    });
    
    // Analyze root cause
    const rootCause = await this.analyzeRootCause(failure, similarFailures);
    
    // Document anti-pattern
    await kb.addLearning({
      problem: failure.description,
      solution: `ANTI-PATTERN: ${failure.approach}\n\nRoot Cause: ${rootCause}\n\nCorrect Approach: ${failure.resolution}`,
      category: 'anti-patterns',
      complexity: failure.complexity,
      tags: ['failure', 'anti-pattern', ...failure.tags],
      metadata: {
        failure_mode: failure.mode,
        impact: failure.impact,
        prevention: this.generatePreventionStrategies(rootCause),
        detection: this.generateDetectionStrategies(failure)
      }
    });
    
    return rootCause;
  }
}

Performance Optimization

Pattern 1: Intelligent Caching

Intent: Cache and reuse knowledge base queries intelligently.

class IntelligentCache {
  private cache = new Map<string, CachedResult>();
  private cacheStats = new Map<string, CacheStats>();
  
  async query(params: QueryParams): Promise<QueryResult> {
    const cacheKey = this.generateCacheKey(params);
    
    // Check cache with intelligence
    if (this.cache.has(cacheKey)) {
      const cached = this.cache.get(cacheKey);
      const stats = this.cacheStats.get(cacheKey);
      
      // Determine if cache is still valid
      if (this.isCacheValid(cached, stats, params)) {
        stats.hits++;
        return cached.result;
      }
    }
    
    // Execute query
    const result = await kb.search(params);
    
    // Intelligent caching decision
    if (this.shouldCache(result, params)) {
      this.cache.set(cacheKey, {
        result: result,
        timestamp: Date.now(),
        params: params
      });
      
      this.cacheStats.set(cacheKey, {
        hits: 0,
        createdAt: Date.now(),
        relevanceDecay: this.calculateDecayRate(params)
      });
    }
    
    return result;
  }
  
  private isCacheValid(cached: CachedResult, stats: CacheStats, params: QueryParams): boolean {
    const age = Date.now() - cached.timestamp;
    const decayedRelevance = 1.0 - (age / 1000 / 86400 * stats.relevanceDecay);
    
    // Consider query specificity and cache hit rate
    const cacheValue = decayedRelevance * (stats.hits / (stats.hits + 1));
    
    return cacheValue > params.min_relevance;
  }
}

Pattern 2: Batch Processing

Intent: Batch related queries for efficiency.

class BatchQueryProcessor {
  private batchQueue: QueryBatch[] = [];
  private batchTimeout: number = 100; // ms
  
  async queueQuery(query: Query): Promise<QueryResult> {
    return new Promise((resolve, reject) => {
      this.batchQueue.push({
        query,
        resolve,
        reject,
        timestamp: Date.now()
      });
      
      if (this.batchQueue.length === 1) {
        setTimeout(() => this.processBatch(), this.batchTimeout);
      }
    });
  }
  
  private async processBatch() {
    const batch = [...this.batchQueue];
    this.batchQueue = [];
    
    // Group similar queries
    const groups = this.groupSimilarQueries(batch);
    
    // Execute grouped queries
    const results = await Promise.all(
      groups.map(group => this.executeGroupedQuery(group))
    );
    
    // Distribute results
    this.distributeResults(batch, results);
  }
}

Pattern 3: Predictive Prefetching

Intent: Prefetch likely next queries based on patterns.

class PredictivePrefetcher {
  private queryPatterns = new Map<string, string[]>();
  
  async queryWithPrefetch(query: Query): Promise<QueryResult> {
    // Execute main query
    const result = await kb.search(query);
    
    // Predict next queries
    const predictions = this.predictNextQueries(query, result);
    
    // Prefetch in background
    this.prefetchInBackground(predictions);
    
    return result;
  }
  
  private predictNextQueries(query: Query, result: QueryResult): Query[] {
    // Based on historical patterns
    const historicalNext = this.queryPatterns.get(query.type) || [];
    
    // Based on result content
    const contentBased = this.analyzeResultsForNextQueries(result);
    
    // Based on user's agent type
    const agentBased = this.getAgentTypicalQueries(query.agentType);
    
    return this.rankPredictions([
      ...historicalNext,
      ...contentBased,
      ...agentBased
    ]);
  }
}

Anti-Patterns to Avoid

Anti-Pattern 1: Knowledge Base Flooding

Problem: Making too many similar queries without analyzing results.

// ❌ BAD: Flooding with variations
async function bad_flooding(error: string) {
  const queries = [];
  for (const word of error.split(' ')) {
    queries.push(kb.search({ query: word }));
  }
  const results = await Promise.all(queries); // Too many queries!
}

// ✅ GOOD: Strategic querying
async function good_strategic(error: string) {
  // Start with full error
  let results = await kb.search({ 
    query: error, 
    min_relevance: 0.8 
  });
  
  // Only broaden if needed
  if (results.results.length === 0) {
    const keywords = extractKeywords(error);
    results = await kb.search({ 
      query: keywords.join(' '),
      min_relevance: 0.7
    });
  }
  
  return results;
}

Anti-Pattern 2: Ignoring Temporal Relevance

Problem: Using outdated solutions without considering recency.

// ❌ BAD: Ignoring time
async function bad_temporal(problem: string) {
  const results = await kb.search({ query: problem });
  return results.results[0]; // Might be years old!
}

// ✅ GOOD: Time-aware search
async function good_temporal(problem: string) {
  // Try recent first
  const recent = await kb.search({ 
    query: problem,
    time_window_days: 30
  });
  
  if (recent.results.length > 0) {
    return recent.results[0];
  }
  
  // Fall back to older with caution
  const older = await kb.search({ query: problem });
  return {
    solution: older.results[0],
    warning: "Solution is older than 30 days, verify compatibility"
  };
}

Anti-Pattern 3: Context Hoarding

Problem: Accumulating too much context without filtering.

// ❌ BAD: Hoarding everything
class BadContextHoarder {
  private context: any[] = [];
  
  async addContext(query: string) {
    const results = await kb.search({ query });
    this.context.push(...results.results); // Grows unbounded!
  }
}

// ✅ GOOD: Intelligent context management
class GoodContextManager {
  private context: PrioritizedContext[] = [];
  private maxContextSize = 10;
  
  async addContext(query: string, priority: number) {
    const results = await kb.search({ query });
    
    // Add with priority
    const newContext = results.results.map(r => ({
      ...r,
      priority: priority * r.relevance,
      addedAt: Date.now()
    }));
    
    // Merge and maintain size limit
    this.context = [...this.context, ...newContext]
      .sort((a, b) => b.priority - a.priority)
      .slice(0, this.maxContextSize);
  }
}

Anti-Pattern 4: Learning Without Validation

Problem: Adding unverified solutions to knowledge base.

// ❌ BAD: Adding without validation
async function bad_learning(problem: string, solution: string) {
  await kb.addLearning({
    problem,
    solution,
    category: 'solution'
  }); // No verification!
}

// ✅ GOOD: Validated learning
async function good_learning(problem: string, solution: string, validation: ValidationResult) {
  if (!validation.success) {
    console.warn("Solution not validated, not adding to KB");
    return;
  }
  
  await kb.addLearning({
    problem,
    solution,
    category: 'solution',
    complexity: validation.complexity,
    tags: [...validation.verifiedAspects],
    metadata: {
      validation_method: validation.method,
      success_metrics: validation.metrics,
      test_coverage: validation.coverage
    }
  });
}

Conclusion

These patterns represent battle-tested approaches for integrating the knowledge base into agent workflows. The key principles are:

1. Context First: Always check what's been done before
2. Progressive Refinement: Start broad, narrow strategically
3. Collaborative Memory: Share and build on collective knowledge
4. Continuous Learning: Document both successes and failures
5. Performance Awareness: Optimize queries and caching

By following these patterns and avoiding the anti-patterns, agents can effectively leverage the full power of the 115,000+ lines of development history in the knowledge base.