#Architecture

Software architecture defines the structural decisions that shape a system's quality attributes: performance, maintainability, and resilience. These posts present architecture patterns, framework comparisons, monolith-to-microservices migration, and the design trade-offs behind production systems.

42 posts tagged with architecture. ← All posts

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Pratik Dhanave · ·9 min read

The End-to-End AI Financial Engineering Stack

Having built every piece — data, pricing, risk, machine learning, documents, underwriting, monitoring, portfolio, governance, memory, production — this closing post assembles them into a coherent whole: the end-to-end AI financial engineering stack. It's a chance to see how the layers fit, where the real value concentrates, and what principles tie the entire discipline together. This is the map of the territory the series has been building, and the judgment to navigate it.

Having built every piece — data, pricing, risk, ML, documents, underwriting, monitoring, portfolio, governance, memory, production — this closing post assembles the end-to-end stack, shows where value really concentrates (reading, judgment-augmentation, institutional memory — not market prediction), and the principles that tie the discipline together. (Educational, not financial advice.)

#AI Financial Engineering#Architecture#Quantitative Finance#AI Engineering
Pratik Dhanave · ·6 min read

Building and Operating an AI Gateway

You've seen what an AI gateway does; the last question is how to get one — build it, adopt an open-source proxy, or use a managed service — and how to run it once you have it. This closing post assembles the full architecture, weighs build-versus-buy honestly, and covers operating the gateway as the critical piece of infrastructure it becomes.

You've seen what an AI gateway does; the last question is how to get one — build it, adopt an open-source proxy, or use a managed service — and how to run it once you have it. This closing post assembles the full architecture, weighs build-versus-buy honestly, and covers operating the gateway as the critical infrastructure it becomes.

#AI Gateway#LLM Infrastructure#AI Engineering#Architecture#LLMs
Pratik Dhanave · ·6 min read

How eBPF Works: Programs, Hooks, Maps

An eBPF program's journey — from code you write to logic running in the kernel — has a few distinct pieces: the program itself, the hook it attaches to, the maps it uses to share data, and the load path that gets it safely into the kernel. Once you can name these four things and see how they fit together, eBPF stops being magic and becomes a system you can reason about.

An eBPF program's journey — from code you write to logic running in the kernel — has a few distinct pieces: the program, the hook it attaches to, the maps it uses to share data, and the load path that gets it safely into the kernel. Once you can name these four things and see how they fit, eBPF stops being magic. With an interactive architecture diagram.

#eBPF#Linux#Systems Programming#Architecture
Pratik Dhanave · ·5 min read

Why You Need an AI Gateway

The moment your application talks to more than one model — or one model but seriously — you accumulate a pile of cross-cutting concerns: provider APIs that differ, outages you must survive, costs you must control, calls you must log. An AI gateway is the single control point that handles all of it, sitting between your applications and every model provider. This series builds one from first principles.

The moment your application talks to more than one model — or one model but seriously — you accumulate cross-cutting concerns: differing provider APIs, outages, costs, logging. An AI gateway is the single control point that handles all of it, sitting between your applications and every model provider. This series builds one from first principles, with interactive architecture diagrams.

#AI Gateway#LLM Infrastructure#AI Engineering#Architecture#LLMs
Pratik Dhanave · ·7 min read

Building Computer-Use Agents — Architecture and the Evolution of Automation

Assembling a computer-use agent means wiring together perception, grounding, action, and the loop into a system that can reliably (enough) operate software — and doing it with the safety and reliability discipline the previous posts demand. This post covers the architecture of a computer-use agent and places it in the larger story of GUI automation: how it evolves from, and improves on, decades of brittle scripting. It's the practitioner's assembly view.

Assembling perception, grounding, action, and the loop into a system — with reliability and safety wrapping as first-class. The architecture of a computer-use agent, and the evolution from brittle scripted RPA to intelligent, adaptive, natural-language-directed automation (with the determinism trade-off).

#Computer-Use Agents#Architecture#RPA#AI Engineering#AI Agents#GUI Automation
Pratik Dhanave · ·6 min read

Architecture and Least Privilege: The Real Defense

Everything before this post raised the probability barrier against injection. This post lowers the impact — and impact is what actually protects you. The load-bearing defense against prompt injection isn't a prompt or a filter; it's an architecture where a fully-hijacked model still can't do anything catastrophic, because it was never granted the power to.

Everything before this raised the probability barrier against injection; this post lowers the impact — and impact is what actually protects you. The load-bearing defense isn't a prompt or a filter but an architecture where a fully-hijacked model still can't do anything catastrophic: least privilege, the confused-deputy trap, human-in-the-loop, and the dual-LLM pattern.

#AI Security#Architecture#Guardrails#LLMs#Prompt Engineering#Prompt Injection
Pratik Dhanave · ·15 min read

Designing a System End to End

The capstone — one problem, a home-timeline feed, designed the whole way through with the method from post one: clarify, estimate, contract, then high-level to deep-dive to bottleneck, naming the trade-off at every step and drawing on all seven earlier posts.

The capstone: one worked design end to end — requirements, estimation, API and data model, high-level architecture, and deep dives applying the whole series (scaling, caching, sharding, consistency, async, reliability) with explicit trade-offs.

#System Design#Architecture#Case Study#Scalability#Distributed Systems#APIs
Pratik Dhanave · ·15 min read

Evolutionary Architecture and Technical Debt

Architecture is never finished. This post is about designing systems for the change you know is coming, guarding the characteristics you care about with automated fitness functions, and treating technical debt as an ongoing budget rather than a someday-rewrite.

Architecture is never done: evolutionary architecture and fitness functions that guard characteristics in CI, technical debt done right (deliberate vs reckless, managing the interest), and incremental strangler-fig migration instead of the doomed big rewrite.

#Software Architecture#Technical Debt#Evolutionary Architecture#Architecture
Pratik Dhanave · ·15 min read

Documenting Architecture

How to communicate an architecture so it survives contact with a real team — a few living, versioned diagrams and decision records instead of a dead 200-page tome nobody opens twice.

Communicating architecture so it survives contact with a team: the C4 model's zoomable levels, diagrams-as-code that live in version control and don't rot, multiple views for multiple audiences, and just-enough living docs plus ADRs.

#Software Architecture#Documentation#C4 Model#Architecture
Pratik Dhanave · ·11 min read

Beyond REST: GraphQL and gRPC

When REST is the wrong shape for the problem, GraphQL and gRPC each fix a different pain — and each buys that fix with a new cost you have to design around.

When REST isn't the right shape: GraphQL (client-selected fields, and its N+1 / caching / complexity costs) and gRPC (Protobuf, HTTP/2, streaming, codegen) — plus a decision framework for REST vs GraphQL vs gRPC.

#API Design#GraphQL#gRPC#Architecture#Streaming#APIs
Pratik Dhanave · ·14 min read

Architecture Patterns

The recurring structural patterns an architect actually reaches for — layered, hexagonal, DDD boundaries, CQRS, event sourcing, saga, strangler fig, and BFF — each with the problem it solves, the cost it charges, and the honest signal that you need it.

The recurring structural patterns and their costs: layered, hexagonal/ports-and-adapters and clean, DDD bounded contexts, CQRS and event sourcing (frequently over-applied), saga, and the strangler fig — apply the simplest that solves the real problem.

#Software Architecture#Design Patterns#DDD#Architecture
Pratik Dhanave · ·13 min read

Architectural Decisions and Trade-offs

The core of the architect's job is not drawing boxes but making, justifying, and recording the significant, hard-to-reverse decisions a system is built on — deliberately, under uncertainty, and with the reasoning written down.

The core of the job: making and recording decisions under uncertainty — one-way vs two-way doors, structured trade-off analysis, avoiding resume-driven development, and Architecture Decision Records (ADRs) that keep the why alive.

#Software Architecture#ADR#Decision Making#Architecture
Pratik Dhanave · ·14 min read

Databases and Storage

Choosing and scaling the data layer without cargo-culting: how to pick relational versus NoSQL by access pattern, why every index is a tax on writes, and why your shard key is the highest-stakes decision you will make.

Choosing and scaling the data layer: relational vs NoSQL by access pattern, indexing (B-tree/hash/LSM), normalization vs denormalization, replication, partitioning/sharding and the shard-key decision, and the distributed-transaction trade-off.

#System Design#Databases#Sharding#Architecture#Distributed Systems
Pratik Dhanave · ·13 min read

Quality Attributes: Architecting for the -ilities

Why the non-functional requirements — performance, scalability, availability, security, maintainability and their kin — are what your architecture is actually optimized for, how to make them measurable, and why they always trade off against one another.

The non-functional requirements that actually drive architecture: the -ilities (performance, scalability, availability, security, maintainability…), making them measurable as scenarios with numbers, and prioritizing the top few because they all trade off.

#Software Architecture#Quality Attributes#NFRs#Architecture
Pratik Dhanave · ·15 min read

Caching

The highest-leverage tool for latency and scale — and the source of its hardest problem, invalidation. Where caches live, the patterns for filling them, how they evict, and why keeping them correct is the part that stays hard.

Caching as the highest-leverage latency tool — and its hardest problem: where caches live, the patterns (cache-aside/read-through/write-through/write-behind), eviction, and invalidation including cache stampede, penetration, and hot keys.

#System Design#Caching#Performance#Architecture#Distributed Systems
Pratik Dhanave · ·13 min read

Architectural Styles

A trade-off-driven tour of the major ways to structure a system — monolith, modular monolith, layered, microservices, service-based, event-driven, and serverless — and how to choose one by team, scale, and organizational maturity rather than hype.

The major ways to structure a system and their trade-offs: the modular monolith (the underrated default), layered, microservices (and their heavy costs), event-driven, and serverless — chosen by team topology and scale (Conway's Law), not hype.

#Software Architecture#Microservices#Monolith#Architecture
Pratik Dhanave · ·14 min read

RESTful Resource Design: Nouns, Methods, and the Maturity Model

How to model resources as nouns, choose HTTP methods by their spec-defined semantics, return the right status codes, and decide honestly how much hypermedia your API actually needs.

Designing REST resources the right way: modeling resources as nouns, URI design, HTTP method semantics (safe vs idempotent, PUT vs PATCH), using status codes correctly, and a realistic take on the Richardson Maturity Model and HATEOAS.

#API Design#REST#HTTP#Architecture#APIs
Pratik Dhanave · ·15 min read

Scaling Fundamentals

How systems grow under load — vertical vs horizontal scaling, why statelessness is the real enabler, load balancing from L4 to L7, consistent hashing, read/write scaling, the scale cube, and when the honest answer is "don't scale yet."

How systems grow: vertical vs horizontal scaling, statelessness as the enabler of horizontal scale, load balancing (L4/L7, consistent hashing), read/write scaling with replicas — and knowing when not to scale.

#System Design#Scalability#Load Balancing#Architecture#Distributed Systems
Pratik Dhanave · ·11 min read

What a Software Architect Does

The opening post of "The Software Architect's Path" — demystifying the role by separating what architecture actually is (the decisions that are hard to reverse) from day-to-day coding, and arguing for the hands-on architect over the ivory-tower one.

The opener to an architect series: what architecture actually is (the significant, hard-to-change decisions), the architect's real responsibilities, the hands-on architect-who-codes model vs the ivory tower, and the myths worth discarding.

#Software Architecture#Career#Leadership#Architecture
Pratik Dhanave · ·13 min read

API Design Principles

The opening post of a series on designing APIs people actually enjoy using — why an API is a contract and a product, the qualities that separate a good one from a bad one, and why the contract should exist before a single line of implementation.

The opener to an API design series: an API is a contract and a product whose users are developers — the qualities that matter (consistency, evolvability, hard-to-misuse), API-first vs code-first, the interface/implementation boundary, and the paradigms you'll weigh.

#API Design#REST#Architecture#Developer Experience#APIs
Pratik Dhanave · ·12 min read

How to Approach System Design

A repeatable method for designing systems and acing the design interview — clarify requirements, estimate on the back of an envelope, pin down the API and data model, then work high-level to deep-dive to bottleneck, always naming the trade-off.

The opener to a system-design series: a repeatable method rather than a grab-bag of components — clarifying functional vs non-functional requirements, back-of-the-envelope estimation with the latency numbers every engineer should know, and the trade-off-driven design flow.

#System Design#Architecture#Scalability#Distributed Systems
Pratik Dhanave · ·7 min read

The Cheapest Reliable Executor Wins

Deterministic rules get first refusal at zero model cost. Only the unknown cases escalate to graduated AI agents. A human approves anything that mutates.

Deterministic rules get first refusal at zero cost and still fire when the model is down; only the novel long tail escalates to graduated agents; a human approves anything that mutates. Build a ladder, not a model call.

#Agentic AI#Cost Optimisation#Architecture#Reliability
Pratik Dhanave · ·8 min read

Building an Agent-Ready Merchant

The surface a store must expose when the buyer is an AI agent, not a browser — and why it is the fintech reliability playbook wearing a new hat.

What a store must expose to sell to agents: a machine-readable product feed/catalog, agentic checkout endpoints, acceptance of delegated payment tokens, idempotency keys for retried agent calls, webhooks for async status…

#Agentic Commerce#Payments#Architecture#Fintech#AI Agents#Distributed Systems#Financial Systems

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.