Context Engineering for Agentic Systems
Context engineering for AI agents: source routing, retrieval scopes, tool results, memory, compression, freshness, and approval-aware context.
May 212 min read
Field guides for agent workflows, MCP tooling, evals, production handoffs, and the architecture behind software that actually ships.
Context engineering for AI agents: source routing, retrieval scopes, tool results, memory, compression, freshness, and approval-aware context.
How to design AI agent memory architecture: short-term state, long-term memory, user preferences, workflow memory, TTLs, privacy, evals, and audit logs.
A production AI agent deployment checklist covering tools, permissions, approvals, evals, observability, rollback, cost limits, security, and repo handoff.
Agent reliability engineering for production AI systems: failure modes, retries, idempotency, evals, rollbacks, observability, human override, and SLOs.
How to design AI agent architecture for regulated industries: data boundaries, approvals, audit logs, explainability, policy checks, evals, and human oversight.
A step-by-step guide to designing an AI agent workflow: trigger, outcome, agents, tools, Skills, MCP, memory, approvals, evals, and production architecture.
How to design MCP server architecture for production AI agents: tools, resources, prompts, auth scopes, approval boundaries, observability, and deployment.
A practical AI agent security architecture for permissions, scopes, approvals, audit logs, tool isolation, secret handling, and prompt injection defense.
A practical architecture for agentic RAG in internal tools: retrieval, tool use, citations, permissions, memory, evals, and human approval.
How an agent workflow turns into a real production system: webhooks, orchestration, queues, memory, tool auth, audit logs, evals, and approval UI.
Why serious AI agent projects should start with workflow design: triggers, tools, model routes, approvals, evals, and architecture.
A practical template for designing AI agents with triggers, tools, model routing, permissions, guardrails, evaluations, and production architecture.
Why serious AI agents need triggers, tools, memory, policies, model routes, approval, evals, and deployment instead of one giant prompt.
Why MCP belongs in agent workflow design, how to model tools and resources, and how to keep connected systems safe.
A practical guide to splitting agents by responsibility without creating a swarm you cannot debug.
How AI agents use tools — function calling APIs, tool definitions, ReAct reasoning loops, tool selection strategies, error recovery, parallel tool calls, and structured outputs with Claude and GPT.
How to orchestrate AI workflows — LLM chains, DAG-based pipelines, conditional branching, human-in-the-loop, error handling, and tools like LangChain, LangGraph, Temporal, and Prefect.
How to evolve APIs safely — additive changes, field deprecation, default values, Postel's law, schema evolution, consumer-driven contracts, and breaking change detection in CI.
How to design batch API endpoints: request patterns, Google-style JSON batching, bulk operations, partial success handling, idempotency in batches, performance trade-offs, and production implementation guidance.
Learn the API composition pattern — aggregating data from multiple services, API gateway composition, GraphQL as composer, BFF pattern, parallel vs sequential calls, timeout handling, and partial failure strategies.
Build robust API error handling — correct HTTP status codes, RFC 7807 Problem Details, error response formats, retry-after, idempotency on errors, client-side handling, logging, and error budgets.
How to adopt API-first design: OpenAPI contracts, mock servers, consumer-driven contracts, API governance, code generation from specs, and building API style guides for consistent developer experiences.
Per-client, per-endpoint, and global rate limiting at the API gateway — sliding windows, quota headers, retry-after, graceful degradation, and tools like Kong and AWS WAF.
Design robust API quota management — throttling vs rate limiting, per-user and per-app quota buckets, quota headers, grace periods, tiered plans, monitoring, and billing integration.
Master async processing patterns — fire-and-forget, request-reply, pub/sub, work queues, delayed processing, batch jobs, long-running tasks, polling vs webhooks, and tools like Celery, Bull, and Temporal.
Domain-Driven Design context maps explained: shared kernel, customer-supplier, conformist, anti-corruption layer, open host service, and published language patterns.
Bulkhead isolation pattern for resilient systems — thread pool, semaphore, and process isolation, resource partitioning, blast radius control, swim lane architecture, cell-based architecture, and tools like Resilience4j and Polly.
Master cache invalidation strategies — TTL-based, event-driven, write-through, cache-aside, tag-based (Surrogate-Key), versioned keys, purge APIs, stale-while-revalidate, and the dogpile effect.
Essential cloud design patterns explained with Azure, AWS, and GCP perspectives: Ambassador, Anti-corruption Layer, CQRS, Event Sourcing, Gateway Aggregation, Retry, and Sharding.
Understand data consistency patterns — strong vs eventual consistency, read-your-writes, causal consistency, bounded staleness, linearizability, and Jepsen testing for distributed systems.
Practical guide to database denormalization — materialized aggregates, embedded documents, precomputed joins, cache tables, and strategies for maintaining consistency when you trade normalization for speed.
Why every microservice needs its own database — data ownership, consistency challenges, saga pattern, API composition, event-driven sync, CQRS, and polyglot persistence explained.
A complete comparison of deployment strategies — rolling update, blue-green, canary, A/B testing, shadow launch, feature flags, and recreate. Learn when to use each and how to choose.
How to run millions of scheduled jobs across a cluster — cron at scale, job deduplication, at-least-once vs exactly-once semantics, priorities, work stealing, and tools like Temporal, Airflow, Quartz, and Hangfire.
Byzantine failures, crash failures, network partitions, clock skew, split brain, cascading failures, gray failures, and detection strategies including heartbeats and phi accrual failure detectors.
How to manage feature toggles at scale: release toggles, experiment flags, ops toggles, permission gates, lifecycle management, and tools like LaunchDarkly and Unleash.
Deep dive into property graph models, Cypher query language, traversal algorithms, Neo4j architecture internals, and real-world use cases for social networks, recommendations, and fraud detection.
How hexagonal architecture separates business logic from infrastructure using ports and adapters. Dependency inversion, testing with fakes, and clean architecture comparison.
A comprehensive guide to micro-batching: how it compares to true streaming, Spark Structured Streaming internals, windowing strategies, exactly-once semantics, and latency tradeoffs.
Build scalable micro frontends with Module Federation, single-spa, Web Components, shared dependencies, independent deployments, routing strategies, and design system integration.
Master microservices communication patterns — REST, gRPC, message queues, event-driven architecture, sagas, service mesh, and resilience patterns like retry, timeout, and circuit breaker.
Monorepo vs polyrepo trade-offs, tooling (Nx, Turborepo, Bazel, Lerna), dependency management, build caching, CI/CD strategies, and code ownership with CODEOWNERS.
Solve the dual-write problem with the transactional outbox pattern — CDC-based outbox with Debezium, polling publisher, inbox pattern for consumers, exactly-once delivery, and idempotent consumers.
Build production PWAs with service workers, cache strategies (cache-first, network-first, stale-while-revalidate), push notifications, installability, Core Web Vitals, and Workbox.
Master queue-based architecture — work queues vs pub/sub, priority queues, delay queues, FIFO guarantees, visibility timeout, poison pill handling, and tools like SQS, Celery, and Bull.
Deep dive into the sidecar container pattern — service mesh sidecars (Envoy), logging and monitoring sidecars, ambassador pattern, init containers, resource overhead, and Kubernetes sidecar injection.
Compare soft delete and hard delete strategies — deleted_at columns, archive tables, event sourcing, cascading deletes, GDPR right to erasure, query performance, and cleanup strategies.
A deep dive into the strangler fig migration pattern: proxy-based routing, feature-by-feature migration, parallel running, data synchronization, monitoring both systems, rollback strategies, and real-world examples from production migrations.
A comprehensive reference of all 250 system design articles organized by category — fundamentals, distributed systems, architecture patterns, interview prep, infrastructure, security, data engineering, and AI/ML.
The capstone milestone: 400 system design articles organized by 12 major categories, key insights, learning paths, and interview preparation strategy. The most comprehensive system design resource on the web.
Master system design tradeoffs — consistency vs availability, SQL vs NoSQL, monolith vs microservices, sync vs async, latency vs throughput, and how to discuss tradeoffs in interviews.
Master all 12 factors of cloud-native application design with modern examples — codebase, dependencies, config, backing services, build/release/run, processes, port binding, concurrency, disposability, dev/prod parity, logs, and admin processes.
How AI is transforming system design — from generating architecture diagrams to deploying full infrastructure. Covers AI-powered design tools, architecture-first development, and the future of system thinking.
Master API design best practices — REST conventions, naming, HTTP methods, pagination, filtering, error responses (RFC 7807), HATEOAS, OpenAPI documentation, versioning, and backward compatibility.
Master caching architecture — cache-aside, write-through, write-behind, read-through patterns. Cache invalidation strategies, TTL vs event-based, and Redis vs Memcached comparison.
Design a calendar system like Google Calendar — event CRUD, recurring events (RFC 5545), time zone handling, availability, scheduling conflicts, invitations, reminders, CalDAV sync, and shared calendars.
Design a collaborative editing system like Google Docs — OT vs CRDT, conflict resolution, cursor presence, version history, WebSocket sync, offline editing, and permission models.
Your free system design education. 100 articles covering fundamentals, patterns, infrastructure, security, data, real-time, career prep, and tools — organized by topic with reading order.
A complete guide to data lake architecture — zones, schema-on-read, file formats (Parquet, ORC, Avro), partitioning, data catalogs, governance, Delta Lake, Apache Iceberg, and the medallion architecture.
Learn when and how to scale your database — vertical vs horizontal scaling, read replicas, sharding strategies, partitioning, and connection pooling with real architecture examples.
The essential patterns behind every large-scale system — CQRS, saga, strangler fig, bulkhead, sidecar, circuit breaker, event sourcing, and more. When to use each with architecture examples.
The essential concepts behind every large-scale system — CAP theorem, consistency models, consensus algorithms, distributed transactions, failure modes, and how to design for reliability.
Deep dive into DNS architecture — record types (A, AAAA, CNAME, MX, TXT, SRV), resolution flow, TTL strategies, GeoDNS, failover, DNSSEC, private zones, and tools like Route 53, Cloudflare DNS, and NS1.
Learn event-driven architecture from first principles — event sourcing, CQRS, message brokers (Kafka vs RabbitMQ vs SQS), saga patterns, and when NOT to use events. With interactive architecture examples.
A comprehensive guide to event sourcing — event stores, projections, snapshotting, CQRS, tools like EventStoreDB, Axon, and Marten, and when event sourcing is the right (or wrong) choice.
Decouple deployment from release with feature flags — percentage rollouts, A/B testing, kill switches, and tools (LaunchDarkly, Unleash, PostHog). Architecture patterns and best practices.
Understand object storage architecture — S3 internals, presigned URLs, multipart uploads, storage tiers, CDN integration, deduplication, and tools like GCS, MinIO, R2, and Backblaze B2.
Design geographically distributed systems with multi-region replication, consistency trade-offs, geo-routing, active-active architectures, conflict resolution, and GDPR-compliant data residency.
The definitive comparison of GraphQL and REST APIs — over-fetching, under-fetching, N+1 queries, caching, tooling, and decision framework. With architecture examples for each.
The definitive guide to scaling strategies — vertical (bigger machines) vs horizontal (more machines). Auto-scaling, stateless design, session affinity, and cost analysis with real examples.
Learn how to design system architecture from scratch. This practical guide covers requirements gathering, component identification, data flow design, and common patterns — with interactive examples.
Choose the right message queue — Kafka for event streaming, RabbitMQ for task queues, SQS for simple async. Patterns: pub/sub, fan-out, dead letter queues, exactly-once delivery.
The definitive guide to choosing between microservices and monolith architecture. Data-driven decision framework with real examples, migration strategies, and interactive architecture comparisons.
Design multi-tenant SaaS systems — shared database, schema-per-tenant, database-per-tenant. Isolation levels, noisy neighbor problems, and tools like Neon and Turso for modern multi-tenancy.
Design a proximity service like Yelp or Google Places — geohash, quadtree, S2 indexing, PostGIS, Redis GEO, Elasticsearch, caching nearby results, and real-time location updates.
Design a distributed rate limiter — token bucket, sliding window log, sliding window counter, Redis-based distributed limiting, race conditions, rate limit headers, and client-side handling.
Build recommendation systems that actually work — collaborative filtering, content-based filtering, hybrid approaches, matrix factorization, deep learning, cold start solutions, and A/B testing strategies.
A deep dive into search engine architecture — inverted indexes, relevance scoring, distributed search, and modern tools like Elasticsearch, OpenSearch, Meilisearch, and Typesense.
Complete guide to secret management — vault architecture, tools (HashiCorp Vault, AWS Secrets Manager, Doppler, 1Password), rotation strategies, dynamic secrets, encryption patterns, and Kubernetes integration.
Complete guide to serverless architecture — AWS Lambda, Cloudflare Workers, Vercel Edge Functions. Patterns for APIs, event processing, real-time, and when serverless is the wrong choice.
How to document architecture that people actually read — C4 diagrams, Architecture Decision Records, README-driven docs, and tools that keep documentation alive.
A comprehensive system design roadmap covering every topic from APIs and databases to ML systems and security. Your complete guide to learning system design, organized into 5 progressive stages.
A practical guide to system design tools — what they do, how to compare them, and which architecture diagram generator fits your workflow.
Practice the most-asked system design interview questions with interactive architecture diagrams. Covers URL shortener, chat app, video streaming, payment systems, and more.
Build reliable webhooks — retry strategies, signature verification, idempotency, fan-out, and monitoring. Patterns from Stripe, GitHub, and Shopify.
Build real-time systems with WebSocket, Server-Sent Events, and long polling. Architecture patterns for chat, notifications, live dashboards, and multiplayer — with scaling strategies.
Implement zero trust security — never trust, always verify. Covers identity-based access, mTLS, least privilege, microsegmentation, and tools (Cloudflare Zero Trust, Tailscale, Istio).
A practical guide to API gateway patterns: routing, rate limiting, authentication, load balancing, and how to design gateway architectures that scale.
The 7 most important backend architecture patterns: monolith, microservices, event-driven, CQRS, serverless, hexagonal, and modular monolith — with real trade-offs.
A practical comparison of GraphQL and REST APIs: when each excels, real-world trade-offs, and how to decide for your next project.
A practical comparison of SQL and NoSQL databases: when to use relational vs document/key-value/graph databases, with real-world examples and trade-offs.
Practical system design advice for startups: when to use a monolith, how to plan for scale without over-engineering, and architecture decisions that save months.
A practical guide to the 12-factor app methodology: codebase, dependencies, config, backing services, build/release/run, and more — with modern examples.
Why manually drawing architecture diagrams is dead. How AI-powered generators like Codelit turn a sentence into a full interactive system blueprint in seconds.
PostgreSQL or MongoDB? Redis or DynamoDB? A practical framework for choosing the right database based on your actual data patterns, not hype.
How to move data from where it is to where it needs to be. A practical guide to batch processing, stream processing, and choosing between them.
How to handle file uploads at scale: chunked uploads, resumable transfers, virus scanning, processing pipelines, and storage strategies.
How does Facebook, Twitter, or LinkedIn generate your personalized feed? Fan-out on write vs read, ranking algorithms, and real-time push updates.
How to build a notification system that handles millions of messages across email, push, and SMS without spamming your users or losing messages.
How to design TinyURL from scratch. Covers ID generation, base62 encoding, caching, analytics, and scaling to billions of redirects.
A practical breakdown of event-driven architecture: what it is, when it shines, when it's overkill, and how to visualize event flows in your system.
A practical guide to using AI tools for system design — from generating architecture diagrams to auditing scaling bottlenecks. Stop drawing boxes by hand.
Your services need to talk to each other asynchronously. Here's when to use Kafka, RabbitMQ, or SQS — and when you don't need a queue at all.
Lambda, Cloud Functions, edge workers — serverless promises infinite scale and zero ops. Here's the reality: when it delivers and when it doesn't.
Your app needs real-time data. Should you use WebSockets, long polling, or Server-Sent Events? Here's the decision framework based on your actual requirements.
The network perimeter is dead. Zero trust assumes every request is a threat. Here's how to implement it without making your developers miserable.
AI writes 41% of all code now. The devs who thrive aren't the ones writing functions — they're the ones designing systems.
You don't need Kubernetes. You don't need microservices. You probably don't even need a message queue. Here's how to know when simple is enough.
The debate is over. The answer isn't 'it depends' — it's 'start with a monolith.' Here's why, with interactive architecture comparisons.