🤖 Agentic AI System Design

Master Agentic AI
System Design

10 real-world AI agent architectures covering orchestration, RAG, memory, sandboxed execution, knowledge graphs, and latency optimization — built for Staff-level interviews.

10 AI Agent Designs
Staff Interview Level
45min Deep Dives
Explore Designs ↓ ← System Design Fundamentals
🤖 Agentic AI

10 AI Agent Architectures

Enterprise-grade designs covering the full spectrum of agentic AI systems — from orchestration to optimization.

Most Likely Staff Level
🤖

AI Agent Orchestration Platform 45 min

Core Product — Design a platform where an AI agent receives natural language requests and executes multi-step workflows across enterprise systems.

"Design a platform where an AI agent receives natural language requests from employees (via Slack/Teams), reasons about what actions to take, executes multi-step workflows across enterprise systems (ServiceNow, Jira, Salesforce, Okta), and returns results. Multi-tenant, reliable, <5 seconds."

Clarifying Questions

  • Scope: Focus on reasoning/LLM layer AND execution infrastructure
  • Latency: Conversational latency <5s
  • Connectors: 5-20 connectors per customer
  • Multi-tenant: Different configs per tenant
  • Permissions: Agent acts as user (user-level permissions)

📊 Back-of-Envelope Estimation

350+ customers × 15K employees = ~5M users. 10% DAU = 500K/day. 3 requests each = 1.5M req/day. Peak QPS ~50. Per request: 1 LLM planning (1-3s) + 2-3 tool calls (200ms-2s) + 1 LLM response. Storage: 10TB/year.

User (Slack/Teams)
  → API Gateway (Auth, Rate Limit, WebSocket)
    → Session Manager (Redis: context, history, user profile)
      → REASONING ENGINE
         ┌───────────────┐
         │ Planning (LLM)│ → Decompose request into steps
         │ Execution Eng │ → Run tool calls with retry/CB
         │ Observation   │ → Evaluate, re-plan if needed
         └───────────────┘
      → Tool Registry (per tenant)
      → State Manager (Redis + PG)
        → ServiceNow, Jira, Salesforce, Okta

Deep Dive 1 — Reasoning Engine (ReAct Pattern)

  • ReAct Pattern: Plan → Execute → Observe loop
  • Model Routing: Fast model for simple queries, powerful model for complex reasoning
  • Plugin Calls: Wrapped with timeout, retry (exponential backoff), circuit breaker, idempotency
  • Parallel Execution: Independent steps run concurrently
  • DLQ: Dead letter queue for failed executions with alerting

Deep Dive 2 — Plugin Registry

  • Plugin Definition: name + description + API endpoint + auth + schema
  • Per-Tenant Instances: Each customer gets their own connector instances
  • Credentials: Stored in HashiCorp Vault with auto-rotation
  • Self-Service UI: Admins configure and manage their own connectors

Deep Dive 3 — State Manager

  • Hot State (Redis): Active conversations, intermediate results
  • Cold Storage (PostgreSQL): Audit trail, compliance logs
  • Resume on Failure: Checkpoint-based recovery for multi-step workflows
  • Debugging: Full execution trace for every request

🚀 Scaling Strategies

  • Parallel tool calls: 50% speedup on multi-step workflows
  • Stream LLM tokens: Perceived latency drops to first-token time
  • Cache user profiles in Redis: Eliminate DB lookups per request
  • Model routing: 70% cheap model = 70% cost savings
  • Circuit breaker per connector: Prevent cascade failures
  • OpenTelemetry tracing: End-to-end observability

🧠 ML Integration

  • Multiple models: NLU classifier, LLM planning, cross-encoder re-ranking
  • Model gateway: A/B testing with statistical significance
  • Canary deployment: 5% → 25% → 100% progressive rollout
  • Data flywheel: User feedback → fine-tuning → better predictions

Key Numbers

  • 5M users, 500K DAU
  • 1.5M req/day, ~50 peak QPS
  • <5s latency end-to-end
  • 10 TB/year storage
Read Full Article →
Cody's Expertise Senior Level
🎫

AI Ticket Triage System 35 min

Cody's Expertise — Automatically route IT support tickets to the correct assignment group with >95% accuracy using collective learning.

"Design a system that automatically routes incoming IT support tickets to the correct assignment group (out of hundreds) with >95% accuracy. Must handle small data per customer and learn across organizations."

Clarifying Questions

  • Groups: 50-500 assignment groups per org
  • Volume: 100-10K tickets/day/org
  • Collective Learning: Yes — shared model across tenants
  • Fields: Short desc, full desc, category, priority, user info
  • Low confidence: Route to human triage queue

📊 Back-of-Envelope Estimation

350 customers × 1K tickets/day = 350K tickets/day. Peak ~15/sec. Inference <100ms (classification, not generation). Nightly batch retraining.

Ticket Created (ServiceNow/Jira)
      │
 Feature Extraction (ALL fields, not just short desc)
      │
 ┌────▼────┐
 │  BERT   │ Fine-tuned multi-class classifier
 │ Encoder │ Input: concatenated ticket fields
 │         │ Output: probability per assignment group
 └────┬────┘
      │
 Confidence Router
 ├── High (>0.95): Auto-route ✓
 ├── Medium (0.7-0.95): Route + flag for review
 └── Low (<0.7): Human triage queue
      │
 Feedback Loop → Correct labels → Retrain

Deep Dive 1 — Feature Engineering

  • Use ALL fields, not just short description
  • Concatenate: short_desc + description + category + subcategory + user_department + user_location + time_of_day
  • Context matters: Same "Cannot connect to VPN" routes differently — Engineering/Remote → Network Security vs Sales/London → EMEA Desktop Support

Deep Dive 2 — Collective Learning

  • Problem: Single org has too few tickets for good model
  • Solution: Pre-train BERT on ALL customers (learn universal patterns)
  • Fine-tune per customer with THEIR specific assignment groups
  • Transfer learning: New customers with 0 tickets get good predictions
  • Privacy: Share model weights, NOT raw ticket data

Deep Dive 3 — Confidence Routing

  • High (>0.95): Auto-route — no human needed
  • Medium (0.7-0.95): Route + flag for review
  • Low (<0.7): Manual triage queue
  • Thresholds tuned per customer based on their accuracy requirements
  • Result: 96% accuracy on routine tickets

🚀 Scaling Strategies

  • Hybrid model: Shared base + per-customer fine-tuning head
  • TensorFlow Serving / Triton: <100ms inference
  • Nightly retraining with latest labeled tickets
  • A/B testing: Compare model versions per customer
  • Monitor: Accuracy, misroute rate, override rate per group

🧠 ML Integration

  • Pre-train BERT on IT ticket corpus (domain-specific language)
  • Per-customer fine-tune: Small dataset per org, shared base weights
  • Metrics: Precision/Recall/F1 per assignment group
  • Data flywheel: More tickets → better model → more auto-routes → more feedback

Key Numbers

  • 350K tickets/day, peak ~15/sec
  • <100ms inference (classification)
  • >95% accuracy target, 96% achieved on routine
  • Nightly retraining cycle
Read Full Article →
Your Experience Senior Level
🔔

Real-Time Notification & Approval System 35 min

Real-World Experience — Design a system where AI agents request approvals from humans, with configurable chains, SLA escalation, and multi-channel delivery.

"Design a system where AI agents can request approvals from humans. Determine approval chain, notify approvers in real-time, track state, escalate overdue approvals, integrate with Slack/Email/Teams."

Clarifying Questions

  • Rules: Admins configure rules per request type
  • Levels: 1-5 approval levels per chain
  • Channels: Slack/Email/Teams/in-app
  • SLA: Configurable (4hrs access, 24hrs procurement)
  • OOO: Delegate to backup approver

📊 Back-of-Envelope Estimation

100K approval requests/day. Avg 2.5 approvers/chain = 250K notifications/day. Delivery <1s. State changes ~500K/day.

Agent Request → Rules Engine → Approval Chain Builder → Kafka Queue
                (determine         (ordered list            │
                 approval           of approvers)      ┌────┼────┐
                 chain)                                 ▼    ▼    ▼
                                                    Slack  Email Teams
                                                    Worker Worker Worker
                                                        │
                                                  State Tracker (Redis + PG)
                                                        │
                                                  Escalation Scheduler
                                                  (check SLA, notify manager)

Deep Dive 1 — Rules Engine

  • Configurable rules: IF request_type='db_access' AND level='production' THEN require VP approval
  • Storage: Rules in PostgreSQL, cached in Redis per tenant
  • Evaluation: Fast rule matching with priority ordering

Deep Dive 2 — Async Notification Pipeline

  • Kafka topic per channel: Slack, Email, Teams each have dedicated topics
  • Dedicated worker pool per channel: Independent scaling
  • Idempotent delivery: Unique notification ID prevents duplicates
  • Delivery tracking: sent → delivered → read → acted-upon

Deep Dive 3 — Escalation Scheduler

  • Check pending every 5 min: Cron job scans for overdue approvals
  • Past SLA: Notify manager or delegate to backup
  • Configurable chain: remind → escalate → auto-approve/reject
  • State machine: submitted → pending → approved/rejected/escalated/expired

🚀 Scaling Strategies

  • Kafka partitioned by customer for ordered processing
  • Worker auto-scaling on queue depth
  • Redis hot state, PG audit trail
  • Exactly-once via idempotency keys

🧠 ML Integration

  • Smart routing: Predict optimal approver based on past patterns
  • SLA prediction: Flag likely-to-breach approvals early
  • Auto-categorization: Classify request type from natural language

Key Numbers

  • 100K approvals/day, 250K notifications
  • <1s delivery latency
  • 500K state changes/day
  • Configurable SLA per request type
Read Full Article →
Your Experience Staff Level
🔌

Multi-Tenant Plugin/Connector Platform 40 min

Enterprise Experience (MuleSoft/Integration) — Design an extensible platform connecting enterprise systems with different APIs, auth methods, and rate limits.

"Design a platform that lets enterprise customers connect their business systems (ServiceNow, Salesforce, Workday, SAP, Okta) to an AI agent. Different systems, API versions, auth methods. Extensible, reliable, secure."

Clarifying Questions

  • Connectors: 100+ pre-built, custom via SDK
  • Auth: OAuth2, API Key, SAML, Basic, mTLS
  • Rate limits: Own rate limits per external system
  • Data residency: Regional deployment requirements
  • Self-service UI: Admins configure without engineering

📊 Back-of-Envelope Estimation

350 customers × 10 connectors = 3,500 instances. 100 API calls/day each = 350K external calls/day. ~1K credential rotations/day. ~50 schema updates/day.

AI AGENT / PLATFORM CORE
  → CONNECTOR GATEWAY (Tenant Resolver, Request Validator, Routing)
    → CONNECTOR RUNTIME ENGINE
       MIDDLEWARE CHAIN: Auth → Rate Limit → Transform → Execute → Retry → Log
    → CONNECTOR REGISTRY (PostgreSQL): Templates, Versions, Schemas
    → CONFIG STORE (PostgreSQL): Per-tenant configs, Field mappings
    → CREDENTIAL VAULT (HashiCorp Vault): OAuth tokens, API keys, mTLS certs
    → EXTERNAL SYSTEMS: ServiceNow, Salesforce, Workday, SAP, Okta
    → CROSS-CUTTING: Rate Limiter (Redis), Circuit Breaker, Metrics (OTel), Audit Log

Deep Dive 1 — Connector Template vs Instance

  • Template = class definition: API spec, auth type, actions, schema
  • Instance = customer-specific: Their URL, credentials, field mappings
  • Analogy: Like Docker Image vs Container

Deep Dive 2 — Middleware Chain (Chain of Responsibility)

  • Auth MW: Inject credentials from vault
  • Rate Limit MW: Sliding window per tenant+connector
  • Transform MW: Map platform schema → vendor schema
  • Execute MW: Make actual API call
  • Retry MW: Exponential backoff, max 3 attempts
  • Log MW: Audit trail for every operation

Deep Dive 3 — Credential Management

  • HashiCorp Vault: Enterprise-grade secrets management
  • Auto-rotation: OAuth2 token refresh before expiry
  • Per-tenant isolation: Separate vault namespaces
  • Zero-trust: Authenticate per request, never cache creds in memory

🚀 Scaling Strategies

  • Stateless workers behind LB: Horizontal scaling
  • Rate limiter per (tenant, connector): Redis sliding window
  • Schema caching: Avoid repeated registry lookups
  • Regional deploy: Data residency compliance

🧠 ML Integration

  • Auto-mapping: LLM suggests field mappings between schemas
  • Health prediction: ML predicts failures from error patterns
  • Usage analytics: Optimize connector configurations

Key Numbers

  • 3,500 connector instances
  • 350K external calls/day
  • ~1K credential rotations/day
  • 100+ pre-built connectors
Read Full Article →
ML Staff Staff Level
🧠

LLM Model Serving & Evaluation Pipeline 40 min

For ML Staff — Design a system serving multiple LLM models with routing, A/B testing, canary deployments, evaluation, and automatic rollback.

"Design a system that serves multiple LLM models (GPT-4, Claude, open-source) for an enterprise AI agent platform. Support model routing, A/B testing, canary deployments, evaluation, and automatic rollback."

Clarifying Questions

  • Models: 5-10 active models simultaneously
  • Latency: <2s first token, <5s full response
  • Eval metrics: Accuracy, latency, cost, hallucination, satisfaction
  • Pipeline: Automated with human approval gate
  • Cost sensitive: Major budget consideration

📊 Back-of-Envelope Estimation

1.5M req/day × 2 LLM calls = 3M calls/day. Avg 700 tokens/call = 2.1B tokens/day. Simple (70%) at $0.002/1K = $2,940/day. Complex (30%) at $0.03/1K = $18,900/day. Total WITH routing: $21,840/day vs WITHOUT: $63,000/day. Savings: $41,160/day (65%).

Agent Request → Model Router → Model Gateway → LLM Provider
                (simple/complex)    │            (OpenAI/Anthropic/Self-hosted)
                              ┌─────▼──────┐
                              │ A/B Testing │
                              │ Framework   │
                              └─────┬──────┘
                              ┌─────▼──────┐
                              │ Evaluation  │
                              │ Pipeline    │
                              └─────┬──────┘
                              Metrics + Alerts → Auto-rollback

3-Tier Model Routing:
  Fast Tier (Llama-3, Mistral):      ~70% traffic
  Mid Tier (Claude Haiku):           ~10% traffic
  Power Tier (GPT-4, Claude Opus):   ~20% traffic

Deep Dive 1 — Model Router

  • Classify complexity: Lightweight classifier (<50ms overhead)
  • Simple (FAQ, password reset): Fast model → low cost
  • Complex (multi-step, ambiguous): Powerful model → high accuracy
  • Fallback: Fast model uncertain → escalate to powerful
  • Impact: 70% simple → 70% cost reduction

Deep Dive 2 — A/B Testing & Canary

  • Progressive rollout: 5% → 25% → 100%
  • Monitor: Accuracy, latency, satisfaction per variant
  • Auto-rollback: On threshold breach for any metric
  • Statistical significance: Testing before promotion

Deep Dive 3 — Evaluation Pipeline

  • Offline eval: 1000+ labeled queries, accuracy, hallucination rate, latency P50/P95/P99, token usage, cost
  • Online eval: Success rate, escalation rate, thumbs up/down
  • Error analysis: Categorization and root cause per failure mode

🚀 Scaling Strategies

  • Response caching: Redis with TTL 1hr for deterministic answers
  • Prompt compression: Reduce token count without losing quality
  • Batching: Group non-urgent requests for throughput
  • Multi-provider distribution: Spread load across providers

🧠 ML Integration

  • Prompt optimization per model version
  • Token budget management per customer
  • Customer-specific fine-tuning
  • Model distillation: Train smaller models on common queries

Key Numbers

  • 3M LLM calls/day, 2.1B tokens/day
  • $21,840/day with routing vs $63,000 without
  • 65% cost savings from model routing
  • <2s first token, <5s full response
Read Full Article →
In JD Senior Level
💾

Conversation Memory & Context System 35 min

In JD: 'Agent Memory' — Design a memory system with short-term, working, and long-term memory for an AI agent. Fast, scalable, privacy-compliant.

"Design a memory system for an AI agent that maintains short-term memory (conversation), working memory (intermediate results), and long-term memory (preferences, past interactions, learned patterns). Fast, scalable, privacy-compliant."

Clarifying Questions

  • Short-term: Session scope (minutes-hours)
  • Long-term: Months-years retention
  • Privacy: Consent-based with retention policies
  • Cross-session recall: Remember past interactions
  • Isolation: User memory private, org patterns shareable

📊 Back-of-Envelope Estimation

500K DAU × 3 conversations × 10 messages = 15M messages/day. 500K active sessions at peak. 5M users × 100 entries = 500M memory records. ~50 req/sec working memory writes. Redis: 24.8 GB. PostgreSQL: 0.93 TB text. Vector DB: 2.85 TB.

┌──────────────────────────────────────────┐
│               MEMORY SYSTEM              │
│  ┌─────────────┐  ┌──────────────────┐  │
│  │ Short-term  │  │ Working Memory   │  │
│  │ (Redis)     │  │ (Redis)          │  │
│  │ Session ctx │  │ Task state,      │  │
│  │ TTL: 24hr   │  │ intermediate     │  │
│  │             │  │ results TTL: 1hr │  │
│  └─────────────┘  └──────────────────┘  │
│  ┌──────────────────────────────────┐   │
│  │ Long-term Memory (PostgreSQL +   │   │
│  │ pgvector)                        │   │
│  │ • User preferences               │   │
│  │ • Past interaction summaries      │   │
│  │ • Learned patterns                │   │
│  │ • Semantic search via embeddings  │   │
│  └──────────────────────────────────┘   │
└──────────────────────────────────────────┘

Deep Dive 1 — Short-Term Memory

  • Full conversation in Redis: {session_id: {messages, user_context, created_at}}
  • TTL 24hr: Auto-expire inactive sessions
  • Context window management: Summarize older messages if exceeds LLM token limit

Deep Dive 2 — Working Memory

  • Intermediate results: During multi-step execution
  • Example: Step 1 returns user's manager → stored → Step 2 uses for approval
  • Redis TTL 1hr: Short-lived, task-scoped
  • Enables re-planning: On failure, retry with intermediate state preserved

Deep Dive 3 — Long-Term Memory

  • PostgreSQL for structured: Preferences, history, feedback
  • pgvector for semantic recall: Embed past interactions, retrieve by similarity
  • Store summaries not raw conversations: Space-efficient and privacy-friendly
  • Privacy: Encryption at rest, retention policies, right-to-delete

🚀 Scaling Strategies

  • Redis cluster sharded by session_id
  • PostgreSQL partitioned by user_id
  • Vector DB sharded by tenant
  • Background summarization job: Async compress conversations to entries

🧠 ML Integration

  • Embed query → find relevant past interactions (cosine similarity)
  • LLM summarizes conversations to structured entries
  • Pattern detection: Proactive suggestions from learned behaviors
  • Memory decay: Reduce relevance score over time

Key Numbers

  • 15M messages/day, 500K concurrent sessions
  • 500M long-term memory records
  • Redis: 24.8 GB, PG: 0.93 TB, Vector: 2.85 TB
  • ~50 req/sec working memory writes
Read Full Article →
In JD Staff Level
📦

Sandboxed Code Execution Environment 35 min

In JD: 'Sandboxed Code Execution' — Design a system where an AI agent generates and executes code on behalf of enterprise users. Sandboxed, time-limited, auditable.

"Design a system where an AI agent can generate and execute code (Python, SQL, shell) on behalf of enterprise users. Sandboxed (can't access other users' data or host), time-limited, auditable."

Clarifying Questions

  • Languages: Python, SQL, shell primarily
  • Data access: Within user's permissions only
  • Timeout: 30s interactive, 5min background
  • Output: Text, tables, charts, files
  • Isolation: Strict multi-tenant isolation

📊 Back-of-Envelope Estimation

50K executions/day. Avg 5s execution. Peak concurrent: 200 sandboxes. Storage: ~50MB/day. Container spin-up target: <2s.

Agent generates code → Code Validator → Sandbox Pool → Execute → Return Output
                       (security scan)   (pre-warmed     (timeout    (text/table/
                                          containers)    enforced)    chart/file)

Deep Dive 1 — Code Validator

  • Static analysis: Block os.system, subprocess, external network calls
  • Whitelist: pandas, numpy, matplotlib (not requests, socket)
  • SQL injection prevention: Parameterized queries only
  • LLM-based review: Second LLM checks code safety before execution

Deep Dive 2 — Sandbox Pool

  • Pre-warmed containers: gVisor or Firecracker microVMs
  • Fresh container per execution: Destroyed after use
  • Resource limits: 1 CPU, 512MB RAM, 100MB disk, internal-only network
  • SIGKILL after timeout: Hard enforcement
  • tmpfs mount: Ephemeral filesystem, nothing persists

Deep Dive 3 — Data Access Layer

  • Proxy between sandbox and enterprise data: No direct access
  • Enforces user permissions: Row-level security
  • SQL: Read-only replica with user's scope
  • Never expose raw credentials: Proxy handles auth
  • Audit log: Every data access recorded

🚀 Scaling Strategies

  • Container pool auto-scales on queue depth
  • Warm pool: 50 pre-initialized containers (<2s cold start)
  • Kafka execution queue: Priority by customer tier
  • Regional execution: Data compliance requirements

🧠 ML Integration

  • Code quality eval: Against test cases
  • Error recovery: LLM analyzes error → generates fix
  • Learning: Track successful patterns → improve code generation prompts

Key Numbers

  • 50K executions/day
  • Peak concurrent: 200 sandboxes
  • <2s container spin-up
  • 30s interactive / 5min background timeout
Read Full Article →
In JD Staff Level
🕸️

Enterprise Knowledge Graph 40 min

In JD: 'Knowledge Graphs' — Design a knowledge graph of org structure, systems, and relationships that the AI agent uses for decision-making.

"Design a knowledge graph representing org structure (people, teams, roles), systems (apps, permissions, data), and relationships. AI agent uses this graph for decisions."

Clarifying Questions

  • Sources: AD, Workday, ServiceNow CMDB, Okta
  • Scale: 10K-100K nodes, 100K-1M edges per customer
  • Queries: "Who is John's manager?", "Who can approve?", "What apps does sales use?"
  • Freshness: Updated within 15 minutes
  • Isolation: Separate graphs per customer

📊 Back-of-Envelope Estimation

350 customers × 50K nodes = 17.5M total nodes. 10 edges/node = 175M edges. 1M graph queries/day (agent lookups). <50ms for 2-hop traversals.

Data Sources → Sync Pipeline → Graph Store → Query Engine → Agent
(AD, Workday,   (CDC/webhooks   (Neo4j or     (Cypher/     (Context for
 Okta, CMDB)     + batch sync)   Neptune)      GraphQL)      reasoning)

Example Graph:
  (John)─[MANAGES]→(Alice)─[HAS_ACCESS_TO]→(Salesforce)─[OWNED_BY]→(Sales Team)

Deep Dive 1 — Graph Schema

  • Node types: Person, Team, Role, Application, Permission, Document, Ticket
  • Edge types: MANAGES, MEMBER_OF, HAS_ROLE, HAS_ACCESS_TO, OWNS, CREATED_BY
  • Example: (John)-[MANAGES]->(Alice)-[HAS_ACCESS_TO]->(Salesforce)-[OWNED_BY]->(Sales Team)

Deep Dive 2 — Sync Pipeline

  • Real-time webhooks: From Workday/Okta/AD for immediate updates
  • Nightly batch full sync: Reconciliation to catch missed events
  • Conflict resolution: Source system is truth, last-write-wins with source priority

Deep Dive 3 — Agent Integration

  • Context queries: Agent queries for user, team, manager, access
  • Approval routing: Traverse MANAGES edge to find approver chain
  • Permission checking: Traverse HAS_ACCESS_TO for authorization
  • Smart suggestions: "Others on your team use X for this"

🚀 Scaling Strategies

  • Graph partitioned by tenant
  • Cache frequent subgraphs: Org hierarchy in Redis
  • Read replicas for queries
  • Index on: email, team_name, app_name

🧠 ML Integration

  • Graph embeddings: For similarity and prediction
  • Link prediction: Predict permissions for new employees
  • Anomaly detection: Unusual permission patterns
  • Community detection: Cross-org implicit teams

Key Numbers

  • 17.5M nodes, 175M edges
  • 1M graph queries/day
  • <50ms for 2-hop traversals
  • 15-minute freshness target
Read Full Article →
In JD Staff Level
⚡

Agent Latency Optimization System 35 min

In JD: 'Latency Optimization' — Reduce AI agent response time from 8-15s to under 3s while maintaining accuracy through caching, parallelism, and model routing.

"The AI agent takes 8-15s to respond. Design a system to reduce to under 3s while maintaining accuracy. Bottlenecks: LLM inference (2-4s), enterprise API calls (1-3s each), cold start for infrequent connectors."

Clarifying Questions

  • Current: LLM planning (2-4s) + tool calls (1-3s × 2-3 steps) + LLM response (1-2s)
  • Cannot sacrifice correctness
  • Can cache deterministic answers
  • Can pre-compute common patterns
  • Streaming acceptable for perceived latency

📊 Back-of-Envelope Estimation

Current: 5-12s. Target: <3s for 80%, <5s for 95%. 1.5M req/day, 70% common patterns.

Request → Query Classifier → Cache Hit? ──Yes──→ Instant Response (<100ms)
                │                  │
                No                 │
                │                  │
         Model Router → Fast Model (simple) → Single Tool Call → Stream Response (<2s)
                │
         Powerful Model (complex) → Parallel Tool Calls + Speculative Execution → Stream (<4s)

Deep Dive 1 — Response Caching

  • Cache common queries: "How do I reset my password?"
  • Cache key: Normalized query + user context hash
  • TTL: 5min dynamic content, 1hr static content
  • Hit rate target: 30-40% of all queries

Deep Dive 2 — Parallel Execution + Streaming

  • Independent steps run simultaneously: asyncio.gather
  • Stream LLM tokens: Word-by-word for perceived speed
  • Speculative execution: Start likely next step before current completes
  • Connection pooling: Avoid TCP handshake overhead

Deep Dive 3 — Model Routing for Speed

  • 70% simple → fast model: 500ms inference
  • 30% complex → powerful: 2-3s inference
  • Classifier overhead: <50ms
  • Low confidence → escalate to powerful model
  • Net effect: 70%×500ms + 30%×2500ms = 1.1s avg (vs 3s before)

🚀 Scaling Strategies

  • Pre-warm connector pools for top-20 enterprise systems
  • Edge caching: Enterprise data closer to compute
  • Prompt compression: Reduce tokens while maintaining quality
  • Queue priority: Interactive requests over background tasks

🧠 ML Integration

  • Query pattern clustering: Identify cacheable categories
  • Latency prediction ML model: Estimate response time upfront
  • Prompt compression training: Reduce tokens intelligently
  • Pre-fetching: Predict needed tools → pre-warm connections

Key Numbers

  • Target: <3s for 80%, <5s for 95%
  • 1.5M req/day, 70% common patterns
  • 30-40% cache hit rate
  • 1.1s avg inference with model routing (vs 3s)
Read Full Article →