#Engineering Practices

Articles about Engineering Practices — exploring patterns, best practices, and real-world implementations in production systems.

16 posts tagged with engineering practices. ← All posts

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Pratik Dhanave · ·14 min read

AI-Assisted Code Review

The series finale — where LLM reviewers genuinely help on every pull request, where they quietly fail, and how to build a human-plus-AI workflow that speeds review up without letting judgment or accountability leak away.

The capstone: using AI/LLM reviewers well and where humans stay essential — AI as a tireless first pass that clears the noise, its limits (design judgment, contextual security, accountability, hallucinations), and a healthy human+AI division of labor.

#Code Review#AI#Automation#Engineering Culture#Engineering Practices#LLMs
Pratik Dhanave · ·14 min read

Code Review at Scale

How to run code review across a whole team or organization without turning it into a bottleneck: treat review latency as a first-class metric, distribute the load, let automation handle the toil, and measure the things that actually predict quality.

Making review work across a team without becoming a bottleneck: review velocity as a first-class metric, ownership and routing (CODEOWNERS), automating toil so humans do judgment, healthy SLAs, and measuring the right things.

#Code Review#Engineering Culture#Automation#Process#Engineering Practices
Pratik Dhanave · ·17 min read

Security and Performance Review

The two specialist lenses a reviewer switches on for a diff — thinking like an attacker to catch the injection and the missing authorization check, and thinking like production to catch the N+1 query — while knowing exactly where the human eye stops and a scanner, profiler, or load test has to take over.

The specialist lenses: security review (untrusted input to a sink, missing authz/BOLA, secrets, SSRF, new dependencies) and performance review (N+1 queries, unbounded queries, missing indexes) — flag the smells, defer depth to scanners and profilers.

#Code Review#Security#Performance#Quality#Engineering Practices
Pratik Dhanave · ·13 min read

The Author's Craft: Making Code Easy to Review

Half of review quality is decided before the reviewer ever opens the diff — by the author. Small focused PRs, a description that states what and why, a self-review pass, and clean commits are how you optimize the one resource a review really spends: the reviewer's attention.

Half of review quality is set before the reviewer arrives: small focused PRs, splitting big changes, great descriptions (what/why/how-to-test), self-review first, and keeping refactor noise out of behavior changes.

#Code Review#Pull Requests#Craft#Collaboration#Engineering Practices#Testing
Pratik Dhanave · ·11 min read

Receiving Feedback Well

The author's side of code review — turning a pull request from an ego threat into a growth loop by separating your identity from your code, responding to every comment, and pushing back with reasoning instead of feelings.

The author's side: separating your identity from your code, assuming good intent, responding to every comment, pushing back with reasoning (not ego), and turning review into a growth loop instead of an ego threat.

#Code Review#Feedback#Growth#Collaboration#Engineering Practices
Pratik Dhanave · ·13 min read

Giving Feedback That Lands

How to write code-review comments that improve the code and the relationship at the same time — commenting on the code not the coder, labeling severity so the author knows what blocks, asking instead of decreeing, teaching the why, and knowing when a thread belongs on a call.

Writing review comments that improve the code and the relationship: comment on the code not the coder, be specific and actionable, label severity (nit/suggestion/blocking), ask questions when you lack context, and pick your battles.

#Code Review#Communication#Feedback#Collaboration#Engineering Practices
Pratik Dhanave · ·14 min read

What to Look For in a Code Review

A reviewer's mental checklist ordered by leverage — design first, then correctness, then tests, then readability, with style and formatting handed off to the tools that were built for it.

A reviewer's checklist ordered by leverage: design first, then correctness and edge cases, then tests, then readability — and what to leave entirely to tools (formatting, lint) so human attention goes where it matters.

#Code Review#Quality#Design#Testing#Engineering Practices
Pratik Dhanave · ·11 min read

Why Code Review

The first post in a practical series on code review — what the practice is actually for, what it is not, and why treating it as a collaboration rather than a gate is what makes it worth the time it costs.

The opener to a code-review series: what review is actually for — catching defects early, better design, knowledge sharing, collective ownership — and what it isn't (gatekeeping, style bikeshedding), plus the healthy team norms that make it work.

#Code Review#Engineering Culture#Collaboration#Quality#Engineering Practices
Pratik Dhanave · ·9 min read

Human-in-the-Loop: An Approval Gate on Durable State

Lesson 7 of Harness Engineering in Go — a sensitive action pauses for a human decision, and the whole suspension is nothing more than a Lesson 2 checkpoint marked awaiting_approval.

Series finale, Lesson 7: a sensitive action pauses for human approval, where suspension is just a Lesson 2 checkpoint marked awaiting_approval, the deadline is checked first so a late yes is void, and the action must be idempotent.

#Harness Engineering#Go#AI Agents#Human-in-the-Loop#Azure#Engineering Practices#Backend Engineering
Pratik Dhanave · ·9 min read

Hierarchical Supervision: Bounded Fan-Out, Ordered Fan-In, Fault Isolation

Lesson 6 of Harness Engineering in Go — a supervisor splits a task, fans out to concurrent workers behind a semaphore, and fans the results back in decomposition order, with each worker's failure (or panic) isolated to one result.

Lesson 6: bounded fan-out behind a semaphore, ordered fan-in via a pre-sized results slice, and per-worker fault isolation so one sub-agent panicking becomes one failed result instead of crashing the whole run.

#Harness Engineering#Go#AI Agents#Concurrency#Azure#Engineering Practices#Backend Engineering
Pratik Dhanave · ·8 min read

Orchestration and Handoff: Routing Intent to a Specialist

Lesson 5 of Harness Engineering in Go — a triage step that first-matches a keyword and hands the request to a specialist, and the exact place a substring table stops being able to think.

Lesson 5: a triage router first-matches a keyword to hand intent to a specialist, and the exact point a substring table stops being able to think.

#Harness Engineering#Go#AI Agents#Orchestration#Azure#Engineering Practices#Backend Engineering#Multi-Agent Systems
Pratik Dhanave · ·7 min read

Advanced Memory: Threads, Keyword Retrieval, and Lossy Summarization

Lesson 4 of Harness Engineering in Go — three collaborating stores (a thread, a knowledge index, and a summarizer) behind interfaces, and an honest accounting of where each local stand-in leaks.

Lesson 4: memory is three stores, not one — an append-only thread, a keyword knowledge index, and a lossy first-and-last summarizer — and an honest account of where each local stand-in leaks against Azure.

#Harness Engineering#Go#AI Agents#Retrieval#Azure#Engineering Practices#Backend Engineering
Pratik Dhanave · ·8 min read

Secure Sandboxing: Running Agent-Written Code Behind a Timeout

Lesson 3 of Harness Engineering in Go — how a context deadline and `exec.CommandContext` reap a runaway snippet, why the two-shaped `Result` distinguishes a timeout from a failure, and the leak that makes a local subprocess a teaching tool, not a security boundary.

Lesson 3: run agent-written code behind a hard timeout with exec.CommandContext, distinguish OK from TimedOut, and face the leak — a subprocess is not a security boundary.

#Harness Engineering#Go#AI Agents#Sandboxing#Azure#Engineering Practices#Backend Engineering
Pratik Dhanave · ·10 min read

Durable Execution: Checkpoint Every Step, Resume After a Crash

Lesson 2 of Harness Engineering in Go — a workflow that saves its progress after each step and picks up exactly where it died, proven by a test that kills a real subprocess mid-run.

Lesson 2: a workflow that checkpoints after every step and resumes from the last one after a crash, why at-least-once execution forces idempotent steps, and the atomic-rename store that survives a killed process.

#Harness Engineering#Go#AI Agents#Durable Execution#Azure#Engineering Practices#Backend Engineering
Pratik Dhanave · ·8 min read

The Agent Harness: guardrails as middleware around the model

Lesson 1 of Harness Engineering in Go — why the input guardrail is a hard block, not a warning, and how a plain `net/http` handler wraps the model call so it tests without a running server.

Lesson 1: why the input guardrail is a hard block rather than flag-and-pass, why it counts runes instead of bytes, and how a plain net/http handler wraps the (stubbed) model call so it tests with httptest.

#Harness Engineering#Go#AI Agents#Prompt Injection#Azure#Engineering Practices#Backend Engineering#Prompt Engineering
Pratik Dhanave · ·7 min read

Harness Engineering in Go: build the harness, then let Azure supply it

Seven patterns that turn a bare model call into production agent infrastructure — each written first as offline Go behind an interface, so the leap to Azure is a swap, not a rewrite.

Seven patterns that turn a bare model call into production agent infrastructure, each written first as offline Go behind an interface (the seam) so the leap to Azure is a swap, not a rewrite.

#Harness Engineering#Go#AI Agents#Microsoft Agent Framework#Azure#Engineering Practices#Backend Engineering

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.