Agent Pseudocode

ALGORITHM: AuthenticateUserINPUT: email (string), password (string)OUTPUT: user (User object) or error
BEGIN    // Validate inputs    IF email is empty OR password is empty THEN        RETURN error("Invalid credentials")    END IF        // Retrieve user from database    user ← Database.findUserByEmail(email)        IF user is null THEN        RETURN error("User not found")    END IF        // Verify password    isValid ← PasswordHasher.verify(password, user.passwordHash)        IF NOT isValid THEN        // Log failed attempt        SecurityLog.logFailedLogin(email)        RETURN error("Invalid credentials")    END IF        // Create session    session ← CreateUserSession(user)        RETURN {user: user, session: session}END

DATA STRUCTURES:
UserCache:    Type: LRU Cache with TTL    Size: 10,000 entries    TTL: 5 minutes    Purpose: Reduce database queries for active users        Operations:        - get(userId): O(1)        - set(userId, userData): O(1)        - evict(): O(1)
PermissionTree:    Type: Trie (Prefix Tree)    Purpose: Efficient permission checking        Structure:        root        ├── users        │   ├── read        │   ├── write        │   └── delete        └── admin            ├── system            └── users        Operations:        - hasPermission(path): O(m) where m = path length        - addPermission(path): O(m)        - removePermission(path): O(m)

PATTERN: Rate Limiting (Token Bucket)
ALGORITHM: CheckRateLimitINPUT: userId (string), action (string)OUTPUT: allowed (boolean)
CONSTANTS:    BUCKET_SIZE = 100    REFILL_RATE = 10 per second
BEGIN    bucket ← RateLimitBuckets.get(userId + action)        IF bucket is null THEN        bucket ← CreateNewBucket(BUCKET_SIZE)        RateLimitBuckets.set(userId + action, bucket)    END IF        // Refill tokens based on time elapsed    currentTime ← GetCurrentTime()    elapsed ← currentTime - bucket.lastRefill    tokensToAdd ← elapsed * REFILL_RATE        bucket.tokens ← MIN(bucket.tokens + tokensToAdd, BUCKET_SIZE)    bucket.lastRefill ← currentTime        // Check if request allowed    IF bucket.tokens >= 1 THEN        bucket.tokens ← bucket.tokens - 1        RETURN true    ELSE        RETURN false    END IFEND

ALGORITHM: OptimizedSearchINPUT: query (string), filters (object), limit (integer)OUTPUT: results (array of items)
SUBROUTINES:    BuildSearchIndex()    ScoreResult(item, query)    ApplyFilters(items, filters)
BEGIN    // Phase 1: Query preprocessing    normalizedQuery ← NormalizeText(query)    queryTokens ← Tokenize(normalizedQuery)        // Phase 2: Index lookup    candidates ← SET()    FOR EACH token IN queryTokens DO        matches ← SearchIndex.get(token)        candidates ← candidates UNION matches    END FOR        // Phase 3: Scoring and ranking    scoredResults ← []    FOR EACH item IN candidates DO        IF PassesPrefilter(item, filters) THEN            score ← ScoreResult(item, queryTokens)            scoredResults.append({item: item, score: score})        END IF    END FOR        // Phase 4: Sort and filter    scoredResults.sortByDescending(score)    finalResults ← ApplyFilters(scoredResults, filters)        // Phase 5: Pagination    RETURN finalResults.slice(0, limit)END
SUBROUTINE: ScoreResultINPUT: item, queryTokensOUTPUT: score (float)
BEGIN    score ← 0        // Title match (highest weight)    titleMatches ← CountTokenMatches(item.title, queryTokens)    score ← score + (titleMatches * 10)        // Description match (medium weight)    descMatches ← CountTokenMatches(item.description, queryTokens)    score ← score + (descMatches * 5)        // Tag match (lower weight)    tagMatches ← CountTokenMatches(item.tags, queryTokens)    score ← score + (tagMatches * 2)        // Boost by recency    daysSinceUpdate ← (CurrentDate - item.updatedAt).days    recencyBoost ← 1 / (1 + daysSinceUpdate * 0.1)    score ← score * recencyBoost        RETURN scoreEND

ANALYSIS: User Authentication Flow
Time Complexity:    - Email validation: O(1)    - Database lookup: O(log n) with index    - Password verification: O(1) - fixed bcrypt rounds    - Session creation: O(1)    - Total: O(log n)
Space Complexity:    - Input storage: O(1)    - User object: O(1)    - Session data: O(1)    - Total: O(1)
ANALYSIS: Search Algorithm
Time Complexity:    - Query preprocessing: O(m) where m = query length    - Index lookup: O(k * log n) where k = token count    - Scoring: O(p) where p = candidate count    - Sorting: O(p log p)    - Filtering: O(p)    - Total: O(p log p) dominated by sorting
Space Complexity:    - Token storage: O(k)    - Candidate set: O(p)    - Scored results: O(p)    - Total: O(p)
Optimization Notes:    - Use inverted index for O(1) token lookup    - Implement early termination for large result sets    - Consider approximate algorithms for >10k results

INTERFACE: AuthenticationStrategy    authenticate(credentials): User or Error
CLASS: EmailPasswordStrategy IMPLEMENTS AuthenticationStrategy    authenticate(credentials):        // Email$password logic        CLASS: OAuthStrategy IMPLEMENTS AuthenticationStrategy    authenticate(credentials):        // OAuth logic        CLASS: AuthenticationContext    strategy: AuthenticationStrategy        executeAuthentication(credentials):        RETURN strategy.authenticate(credentials)

CLASS: EventEmitter    listeners: Map<eventName, List<callback>>        on(eventName, callback):        IF NOT listeners.has(eventName) THEN            listeners.set(eventName, [])        END IF        listeners.get(eventName).append(callback)        emit(eventName, data):        IF listeners.has(eventName) THEN            FOR EACH callback IN listeners.get(eventName) DO                callback(data)            END FOR        END IF