Tag
A seasoned AI engineer shares key skills for 2026, including systematic output reading, context engineering, tool description discipline, eval design, model routing, prompt versioning, confidence scoring, streaming architecture, fallback chains, latency budgets, failure cataloguing, agent-vs-workflow decisions, and failure post-mortems as portfolio content.
A thread explaining six essential AI concepts (tokens, embeddings, vector search, etc.) for building production-ready AI systems, emphasizing that understanding them prevents costly failures like runaway API costs.
This thread presents a comprehensive guide to context engineering for AI agents, explaining why context management is critical for agent performance and how to optimize token usage to avoid degradation.
GSD Core is an open-source workflow framework that solves the problem of context pollution and code quality degradation in AI coding tools like Claude Code during long tasks through a five-step cycle of 'Discussion → Planning → Execution → Verification → Delivery' and sub-agents with independent contexts. It has gained 64K Stars on GitHub.
This paper evaluates context engineering configurations for LLM agents in enterprise tool-use workflows, showing that summarization with selective pruning achieves 91.6% accuracy while reducing token usage by over 60% compared to full-context baselines.
Datadog's AI report highlights that senior engineers who understand AI systems, including multi-model routing, reliability issues, observability, context engineering, and compound engineering, will have a significant advantage.
Explains how a traditional backend inflates AI agent token usage and demonstrates a context-engineering approach that reduces Claude Code session costs by 2.5x without changing models or prompts.
awesome-harness-engineering is a curated list of resources on AI agent harness engineering (context management, tool design, verification loops, memory systems, etc.) from companies like OpenAI, Anthropic, Microsoft, and Meta, aimed at helping developers build reliable agent frameworks.
Sentra CEO Ashwin Gopinath argues that memory for AI agents should be defined as retained consequence, where purpose determines which past information influences future behavior, distinguishing knowledge, context, and memory in enterprise settings.
KACE introduces a knowledge-adaptive context engineering method that separates storage from usage via an epistemic tree and tiered self-consistency, achieving 62.2% on AIME 2025—a 10.4-point gain over fixed self-consistency.
The article by Leonie Monigatti discusses the role of agentic search in context engineering for AI agents, tracing the evolution from fixed RAG pipelines to agentic RAG and context curation. It provides intuition on the strengths and weaknesses of various search tools used in agentic systems.
This article by yan5xu (former ManusAI) proposes a spiral evolution model for LLM engineering paradigms: from Prompt Engineering (2022-2024) to Context Engineering (2025), then to Harness Engineering (2026-), and discusses the bottlenecks and driving factors at each stage.
The article discusses three stages of LLM engineering evolution from Prompt Engineering to Harness Engineering, reflecting the progression of AI engineering practices.
A Hugging Face blog post that defines and clarifies key terms in the AI agent field, such as scaffolding, harness, context engineering, and tool use, aiming to standardize vocabulary amidst rapid evolution.
A detailed analysis of five AI freelance skills (context engineering, agent orchestration, AI pipeline architecture, voice/brand replication, AI cost engineering) that are already commanding $200-$500/hour in 2026 and expected to become baseline for senior AI freelancers by January 2027.
A comprehensive guide to AI agents, covering the basics, the ReAct loop, task decomposition, context engineering, and the autonomy spectrum, aimed at both beginners and those building production systems.
This tool provides context engineering for AI coding agents by converting any codebase into an interactive graph that agents can query, compatible with Claude Code, Codex, and Antigravity, and is 100% open source.
Based on 1281 agent runs, Sourcegraph found that the main reason coding agents fail in large codebases is insufficient infrastructure, not model capability. The typical failure mode is "lost in the codebase," requiring improvements in code retrieval, navigation, and context engineering.
Akshay Pachaar clarifies three distinct AI engineering concepts — prompt engineering (the message), context engineering (the memory), and harness engineering (the machine) — explaining their roles and interplay in building LLM-based agents, with a link to a deeper article on agent harness engineering.
Context engineering is identified as the most critical area for AI agent success, with the assertion that models are already capable but fail due to inadequate context. The thread outlines four key ingredients for effective context.