#Engineering Judgment

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

7 posts tagged with engineering judgment. ← All posts

#A/B Testing (3)#A2A (14)#ADK (8)#AG-UI (6)#AI (9)#AI Agents (335)#AI Architecture (22)#AI Cost (10)#AI Cost Optimization (8)#AI Engineering (282)#AI Evaluation (9)#AI Financial Engineering (14)#AI Gateway (8)#AI Governance (40)#AI Red Teaming (9)#AI Research (9)#AI Safety (12)#AI Security (29)#AI in Production (12)#AML (3)#API Design (10)#API Security (8)#APIs (59)#AWS (17)#Accountability (8)#Accounting (9)#Agent Skills (11)#Agentic AI (25)#Agentic Commerce (12)#Agentic RAG (8)#Agents (4)#Amazon Bedrock (8)#Analytics (3)#Architecture (42)#Attention (3)#Audit (4)#Authentication (11)#Authorization (3)#Automation (11)#Azure (11)#Azure AI Foundry (9)#Backend Engineering (310)#Benchmarks (3)#Best Practices (4)#BigQuery (6)#Business Finance (8)#Business Strategy (55)#C (8)#CI/CD (16)#Caching (11)#Capital Markets (14)#Card Payments (12)#Cards (13)#Career (25)#Causal Inference (8)#Cellular Automata (8)#Checkpointing (4)#Claude Code (8)#Cloud (5)#Cloud Architecture (3)#Cloud Native (10)#Code Review (9)#Coding Agents (8)#Collaboration (5)#Communication (9)#Complexity (8)#Compliance (54)#Computer Networking (9)#Computer Science (40)#Computer Vision (15)#Computer-Use Agents (8)#Concurrency (40)#Consulting (5)#Containers (10)#Context Engineering (14)#Conversational AI (8)#Cost Optimisation (5)#Cost Optimization (3)#Credit (16)#Credit Risk (16)#CrewAI (8)#Crypto (12)#Cryptocurrency (12)#Cryptography (9)#Custody (9)#DSPy (8)#Data (14)#Data Engineering (24)#Data Science (8)#Data Structures (10)#Databases (41)#Deep Learning (20)#Deployment (5)#Design (3)#Design Patterns (10)#DevOps (24)#DevSecOps (22)#Developer Experience (5)#Developer Tools (5)#Differential Privacy (3)#Distributed Systems (100)#Documentation (3)#Edge AI (8)#Embeddings (21)#Emotional Intelligence (8)#Energy (8)#Engineering (14)#Engineering Culture (3)#Engineering Judgment (7)#Engineering Practices (16)#Error Handling (4)#Evaluation (68)#Event-Driven Architecture (8)#Explainable AI (8)#FREE-AI (8)#FX (5)#Feature Engineering (3)#Feedback (4)#FinOps (23)#FinTech (6)#Financial AI (14)#Financial Engineering (8)#Financial Systems (129)#Fine-Tuning (12)#Fintech (131)#Flutter (8)#Forecasting (10)#Foreign Exchange (5)#Forward Deployed Engineer (22)#Forward Deployed Engineering (8)#Fraud (10)#Function Tools (5)#Functional Programming (3)#Fundamentals (23)#Fundraising (8)#GCP (5)#GPU (8)#GUI Automation (8)#Gemma (4)#Generative AI (3)#Git (8)#Go (220)#Go-to-Market (13)#Google ADK (36)#Governance (61)#Granite (6)#Graph Neural Networks (8)#GraphQL (3)#Growth (3)#Guardrails (34)#HIPAA (3)#HTTP (3)#Harness Engineering (8)#Hiring (8)#Hugging Face (8)#Human-in-the-Loop (9)#IBM watsonx (8)#Identity (11)#Inference (8)#Information Retrieval (8)#Integration (3)#Intellectual Property (8)#Interfaces (3)#Interpretability (8)#JavaScript (8)#KYC (11)#KYC and AML (12)#Kafka (11)#Knowledge Graphs (10)#Kubernetes (17)#LLM (5)#LLM Inference (8)#LLM Infrastructure (8)#LLM-as-Judge (3)#LLMOps (8)#LLMs (192)#LangChain (8)#LangGraph (11)#Leadership (29)#Ledger (12)#Legal (8)#Lending (14)#Linux (9)#LlamaIndex (8)#Load Balancing (3)#MCP (22)#MLOps (36)#Machine Learning (109)#Marketing (16)#Markets (4)#Mathematical Finance (8)#Memory (15)#Memory Management (5)#Metrics (7)#Microservices (3)#Microsoft Agent Framework (150)#Middleware (6)#Migration (9)#Mixture of Experts (5)#Model Risk (3)#Monitoring (5)#Multi-Agent (10)#Multi-Agent AI (14)#Multi-Agent Systems (73)#Multimodal (3)#Multimodal AI (11)#NIM (5)#NVIDIA (8)#Networking (3)#OAuth (3)#OWASP (7)#Observability (50)#On-Device AI (8)#Ontology (8)#Open Source (7)#OpenTelemetry (5)#Operating Systems (9)#Operations (13)#Opinion (6)#Orchestration (10)#Organizational Design (8)#Payment Rails (16)#Payments (54)#People (8)#Performance (56)#Personalization (9)#Platform Engineering (9)#Policy as Code (3)#PreSales (8)#Privacy (13)#Privacy Engineering (3)#Process (4)#Product (30)#Product Management (8)#Production (22)#Programming (10)#Programming Languages (48)#Prompt Engineering (80)#Prompt Injection (15)#Protocol Buffers (3)#Protocols (9)#Providers (4)#Pydantic AI (8)#Python (142)#Quality (3)#Quantitative Finance (22)#RAG (70)#RBI (3)#REST (5)#Rails (16)#Real-Time (8)#Reasoning Models (8)#Recommender Systems (8)#Reconciliation (3)#RegTech (8)#Regulation (10)#Reinforcement Learning (9)#Reliability (56)#Resilience (4)#Responsible AI (5)#Retrieval (3)#Risk (14)#Risk Management (11)#Rust (32)#SLSA (3)#SRE (23)#Sales (9)#Scalability (6)#Search (10)#Security (102)#Security Engineering (8)#Self-Evolving Agents (16)#Semantic Web (8)#Sessions (3)#Settlement (9)#Soft Skills (8)#Software (3)#Software Architecture (36)#Software Delivery (9)#Software Engineering (152)#Spanner (4)#Speech (8)#Startups (30)#Statistics (8)#Strands (8)#Strategy (6)#Stream Processing (8)#Streaming (36)#Structured Output (4)#Supply Chain Security (9)#Sustainability (8)#System Design (32)#Systems Programming (56)#Testing (66)#Threat Modeling (3)#Time Series (9)#Tool Use (23)#Tooling (5)#Tools (4)#Trading (8)#Treasury (6)#Trust (3)#Type System (3)#Type Systems (11)#TypeScript (8)#Validation (3)#Valuation (3)#Vector Databases (22)#Vector Search (11)#Venture Capital (8)#Version Control (9)#Voice AI (9)#Web Development (6)#Workflow (4)#Workflows (14)#eBPF (8)#gRPC (13)#smolagents (8)
Pratik Dhanave · ·8 min read

Coding Agents in Practice — Where They Shine, Where They Don't

Coding agents are genuinely transformative on the right tasks and frustrating on the wrong ones, and knowing the difference is the practical skill that separates productive use from disappointment. This closing post is the honest field guide: where coding agents excel, where they struggle, how to work with them well, and where software engineering is heading. It turns the series into judgment you can apply today.

Coding agents are transformative on the right tasks and frustrating on the wrong ones. Where they shine (well-scoped, verifiable, groundable, reviewable work), where they struggle (ambiguous, weakly-verified, large/architectural), how to work with them well (scope, verify, ground, review, delegate the mechanical), and where software engineering is heading.

#Coding Agents#AI Engineering#Software Engineering#Engineering Judgment#AI Agents
Pratik Dhanave · ·7 min read

Computer-Use Agents in Practice — Where They Fit and Where They're Going

Computer-use agents are a genuine frontier capability — and also one of the most over-hyped and under-reliable, so knowing where they actually fit today is the practical skill. This closing post is the honest field guide: the real use cases, the honest limits, how to deploy them responsibly, and where the technology is heading. It turns the series into judgment about when to reach for an agent that drives the screen.

A frontier capability that's also over-hyped and under-reliable. Where computer-use agents actually fit (API-less software, cross-app workflows, assistive/supervised use), the honest limits (prefer an API when one exists; not ready for long unattended high-stakes autonomy), how to deploy responsibly, and the trajectory.

#Computer-Use Agents#AI Engineering#Automation#Engineering Judgment#AI Agents#GUI Automation#APIs
Pratik Dhanave · ·6 min read

When to Use Reinforcement Learning — Power, Cost, and Honest Judgment

Reinforcement learning is the most seductive paradigm in machine learning: define a goal, let an agent figure out the rest. It's also the hardest to get working and the easiest to misapply. This closing post is the practical judgment the series was building toward — where RL genuinely shines, where a simpler method is the honest choice, and how to recognize which situation you're in. It's the difference between RL as a tool and RL as a trap.

RL is the most seductive ML paradigm and the easiest to misapply. Where it genuinely shines (games, control, alignment), where a simpler method is the honest choice, and how to tell which — the difference between RL as a tool and RL as a trap. Its deepest value: optimizing goals you can judge but not specify.

#Reinforcement Learning#Engineering Judgment#Strategy#AI Engineering#Machine Learning
Pratik Dhanave · ·7 min read

Applications and When to Use GNNs

Graph neural networks are genuinely transformative for the right problems and needless complexity for the wrong ones. This closing post surveys where GNNs deliver real value — drug discovery, recommendation, fraud detection, knowledge graphs — and, just as importantly, gives the honest decision framework for when a GNN is worth its cost versus when a simpler approach wins. It turns the series into a judgment you can apply.

GNNs are transformative for the right problems (drug discovery, recommendation, fraud rings, knowledge graphs) and needless complexity for the wrong ones. The honest decision framework: use a GNN when structure carries signal and it beats a structure-ignorant baseline — and reach for something simpler when it doesn't.

#Graph Neural Networks#Applications#Engineering Judgment#Machine Learning#Deep Learning
Pratik Dhanave · ·7 min read

Interpretability in Practice — Choosing Methods and Avoiding Traps

Interpretability has a toolbox — intrinsic models, feature importance, LIME, SHAP, counterfactuals, saliency maps — but knowing the tools isn't the same as using them wisely. The real skill is matching the method to the need, reading explanations with appropriate skepticism, and avoiding the traps that turn interpretability into false confidence. This closing post is the practical judgment the series was building toward: how to actually do interpretability without fooling yourself.

Knowing the tools isn't using them wisely. Matching the method to the need (model, level, audience), the traps that cause false confidence (explanations aren't truth, explanation isn't causation, plausible isn't correct, interpretability-washing), and the skepticism that separates genuine understanding from a rubber stamp.

#Explainable AI#Interpretability#Engineering Judgment#AI Governance#Machine Learning
Pratik Dhanave · ·7 min read

Forecasting in Practice — Choosing Methods and Shipping Forecasts

Knowing the methods is the easy part; building a forecasting system that's accurate, honest, and maintainable is the real job. This closing post assembles the series into practical judgment — how to choose a method, how to ship forecasts that keep working, and the hard-won lessons (start simple, respect baselines, monitor for drift) that no single technique teaches. It turns the series into something you can actually run.

Knowing the methods is easy; shipping accurate, honest, maintainable forecasts is the job. The complexity ladder (naive → classical → ML → deep, stopping when complexity stops helping), the production realities (honest backtesting, calibrated uncertainty, monitoring for drift and shocks), and the hard-won lessons: start simple, respect baselines, stay humble.

#Time Series#Forecasting#Engineering Judgment#MLOps#Machine Learning
Pratik Dhanave · ·7 min read

Computer Vision in Practice — Shipping Systems That See

Building a computer-vision system that works in a demo is one thing; building one that works reliably in the real world — at the right speed, cost, and robustness — is another. This closing post assembles the series into practical judgment: how to approach a vision problem, the deployment realities that trip teams up, and where the field is heading. It turns pixels-to-understanding from a concept into something you can actually ship.

Building a vision demo is one thing; shipping a reliable system at the right speed, cost, and robustness is another. A workflow for approaching a vision problem, the deployment realities (efficiency for real-time/edge, robustness to distribution shift, edge cases, monitoring), and where the field is heading — multimodal and foundation models.

#Computer Vision#Deployment#Engineering Judgment#Multimodal AI#Deep Learning#Machine Learning

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.