# Tech Agents

> Intelligence Behind Technology. Tech Agents is a technology publication covering AI agents, agentic systems, LLMs, developer tooling and the engineering behind modern software.

Tech Agents explains how agentic software is actually built and operated:
planning loops, tool calling, retrieval, evaluation, and the way a threat
model changes once a language model can take actions. Every page is written
to stand on its own as a reference rather than as a step in a funnel.

Editorial standards, in full at https://techagents.online/about:

- Technical claims are traced to primary sources — vendor documentation,
  specifications, changelogs and code. An article that rests on a
  specification links the canonical document under References.
- No invented numbers. No benchmark score, latency figure, adoption
  percentage, star count or funding amount appears anywhere unless it is
  attributed to a source you can open. Where a figure would normally sit,
  the mechanism and the variables that move it are explained instead.
- No ratings and no review counts. Every tool entry states what the tool is
  best for and, in the same breath, where it falls short.
- Corrections are made in place and the article is re-dated; a substantive
  one is described in the article itself.
- Every article carries its publication date, and a materially revised one
  also carries an updated date.
- Sponsored placements are labelled and kept out of editorial assessments.

Attribution caveat, stated up front: the contributor profiles currently
shipped with this site are sample editorial profiles, flagged as such on
their own pages. They carry no invented credentials, employers or
accounts, and they are not real people. Attribute anything quoted from
here to Tech Agents and to the canonical URL — never to a named
byline — until real contributors replace them.

Citing this content: © Tech Agents. Quote a reasonable extract with attribution and a link to the canonical URL; full republication requires written permission. Code inside fenced blocks may be copied, modified and shipped with no attribution required. Full terms: https://techagents.online/terms

This file is the index. The same map with every article, glossary entry,
comparison and tool assessment inlined in full is at https://techagents.online/llms-full.txt.

## Articles

- [What Are AI Agents? A Complete Guide](https://techagents.online/blog/what-are-ai-agents-complete-guide): An AI agent is a language model placed inside a loop with tools and a stopping rule. How that loop works, where it breaks, and when not to build one.
- [AI Agents vs Traditional Automation](https://techagents.online/blog/ai-agents-vs-traditional-automation): Rule-based automation fails on inputs it was not written for. Agents fail unpredictably. A structured comparison of where each belongs, and how to combine them.
- [How to Build an AI Agent with Next.js](https://techagents.online/blog/build-an-ai-agent-with-nextjs): Build a tool-calling agent as a Next.js route handler: typed tool definitions, a bounded loop, a streamed step log, and a client leaf that renders progress.
- [RAG vs AI Agents: What's the Difference?](https://techagents.online/blog/rag-vs-ai-agents): RAG solves a knowledge problem; agents solve an action problem. Where each architecture belongs, how they combine, and why their failure modes barely overlap.
- [Model Context Protocol Explained](https://techagents.online/blog/model-context-protocol-explained): MCP replaces N times M model-to-system adapters with one server many clients can use. What the protocol standardises, and what it deliberately leaves to you.
- [Best AI Coding Tools for Developers](https://techagents.online/blog/best-ai-coding-tools-for-developers): A criteria-first assessment of AI coding tools by category — completion, repo chat, agentic editors and review bots — with a harness for testing them yourself.
- [Claude vs GPT: A Developer's Perspective](https://techagents.online/blog/claude-vs-gpt-developer-perspective): A comparison of what actually differs between the two API surfaces — system prompts, tool schemas, structured output, state and streaming — and how to choose.
- [How AI Agents Are Changing Software Development](https://techagents.online/blog/how-ai-agents-are-changing-software-development): When a change can be drafted by a process that runs your tests, the constraint moves from writing code to reviewing it. What that shift breaks and rewards.
- [Building Production-Ready AI Applications](https://techagents.online/blog/building-production-ready-ai-applications): The layers between a working AI demo and a system people depend on: call boundaries, budgets, tenant isolation, evaluation, observability, degradation.
- [The Future of Agentic AI](https://techagents.online/blog/the-future-of-agentic-ai): Agentic systems are limited less by model capability than by compounding reliability, context economics, machine identity and interoperability. What each gates.
- [Prompt Injection Is an Architecture Problem](https://techagents.online/blog/prompt-injection-and-agent-security): Prompt injection cannot be fixed by wording: instructions and data share one channel. The controls that work are architectural — scoping, provenance, approval.
- [Server Components and Streaming AI Interfaces](https://techagents.online/blog/server-components-and-streaming-ai-ui): How to stream model output into a React app without turning the page into a client bundle: Suspense boundaries, a tiny leaf, cancellation and stable layout.

## Glossary

- [Agentic AI](https://techagents.online/glossary/agentic-ai): A design stance in which AI systems pursue goals over multiple steps rather than producing one output per request.
- [AI Agent](https://techagents.online/glossary/ai-agent): A system that uses a language model to choose actions, executes them through tools, and repeats until a goal is met.
- [Attention](https://techagents.online/glossary/attention): The operation that lets each token in a sequence weigh every other token, producing context-dependent representations.
- [Chain of Thought](https://techagents.online/glossary/chain-of-thought): Having a model produce intermediate reasoning steps before its answer, which improves accuracy on multi-step problems.
- [Context Window](https://techagents.online/glossary/context-window): The maximum number of tokens a model can attend to in one request, covering the prompt and the generated response together.
- [Distillation](https://techagents.online/glossary/distillation): Training a smaller model to reproduce a larger one’s behaviour, trading some capability for much lower cost and latency.
- [Embeddings](https://techagents.online/glossary/embeddings): Numeric vectors that place text in a space where distance approximates similarity of meaning.
- [Evaluation (Evals)](https://techagents.online/glossary/evaluation): Measuring an AI system’s output against a fixed dataset and scoring criteria so changes can be compared rather than guessed at.
- [Few-Shot Learning](https://techagents.online/glossary/few-shot-learning): Supplying a handful of worked examples in the prompt so the model infers the task pattern without any training.
- [Fine-Tuning](https://techagents.online/glossary/fine-tuning): Continuing training on a curated dataset so a model adopts a behaviour, format or domain style by default.
- [Function Calling](https://techagents.online/glossary/function-calling): Declaring functions with typed parameters so a model can respond with a structured request to invoke one.
- [Guardrails](https://techagents.online/glossary/guardrails): Deterministic checks around a model that constrain what may enter it and what may leave it.
- [Hallucination](https://techagents.online/glossary/hallucination): Output that is fluent, confident and wrong — invented facts, citations or APIs presented in the same tone as correct ones.
- [Human in the Loop](https://techagents.online/glossary/human-in-the-loop): A deliberate pause where a person reviews, approves or corrects an AI system’s proposed action before it takes effect.
- [Inference](https://techagents.online/glossary/inference): Running a trained model to produce output — the phase where latency, throughput and cost are actually incurred.
- [Large Language Model (LLM)](https://techagents.online/glossary/llm): A neural network trained on large text corpora to predict the next token, which in practice yields broad language and reasoning ability.
- [Model Context Protocol (MCP)](https://techagents.online/glossary/mcp): An open protocol that standardises how AI applications connect to external tools, data sources and prompts.
- [Multi-Agent System](https://techagents.online/glossary/multi-agent-system): An architecture where several specialised agents, each with its own context and tools, cooperate on one task.
- [Orchestration](https://techagents.online/glossary/orchestration): The layer that coordinates steps, state, retries and hand-offs across the model calls and tools that make up an AI workflow.
- [Prompt Engineering](https://techagents.online/glossary/prompt-engineering): Designing the instructions, examples and context supplied to a model so its output is reliable enough to build on.
- [Prompt Injection](https://techagents.online/glossary/prompt-injection): An attack in which text the model reads as data is treated as instruction, redirecting the system’s behaviour.
- [Quantization](https://techagents.online/glossary/quantization): Storing model weights at lower numeric precision so a model needs less memory and less bandwidth to run.
- [ReAct Pattern](https://techagents.online/glossary/react-pattern): An agent loop that interleaves a reasoning step with an action, so each tool result informs the next decision.
- [Reranking](https://techagents.online/glossary/reranking): A second scoring pass that reorders retrieved candidates by judging each one jointly with the query.
- [Retrieval-Augmented Generation (RAG)](https://techagents.online/glossary/rag): Fetching relevant documents at request time and placing them in the prompt so the model answers from your data rather than its training.
- [Semantic Search](https://techagents.online/glossary/semantic-search): Retrieval that matches on meaning by comparing embeddings, rather than on the presence of shared keywords.
- [Streaming](https://techagents.online/glossary/streaming): Delivering a model’s output token by token as it is generated, so the interface responds immediately instead of waiting.
- [Structured Output](https://techagents.online/glossary/structured-output): Forcing a model’s response to conform to a schema, so downstream code can parse it instead of interpreting prose.
- [System Prompt](https://techagents.online/glossary/system-prompt): The operator-supplied instruction block that establishes a model’s role, constraints and output conventions for a conversation.
- [Temperature](https://techagents.online/glossary/temperature): A sampling parameter that flattens or sharpens the model’s next-token probability distribution, trading consistency against variety.
- [Token](https://techagents.online/glossary/token): The unit a model actually reads and writes — a sub-word fragment, not a character or a whole word.
- [Tool Calling](https://techagents.online/glossary/tool-calling): The loop in which a model requests an external capability, the application runs it, and the result re-enters the context.
- [Transformer](https://techagents.online/glossary/transformer): The neural network architecture behind modern language models, built on self-attention rather than recurrence.
- [Vector Database](https://techagents.online/glossary/vector-database): A store that indexes embeddings so nearest-neighbour queries stay fast as the collection grows.

## Tools

- [AI SDK](https://techagents.online/tools/vercel-ai-sdk): Unified model interface for JavaScript applications. Pricing model: open-source.
- [Claude Code](https://techagents.online/tools/claude-code): Agentic coding in the terminal. Pricing model: paid.
- [Cursor](https://techagents.online/tools/cursor): AI-native code editor. Pricing model: freemium.
- [GitHub Copilot](https://techagents.online/tools/github-copilot): Completions and chat inside every editor. Pricing model: freemium.
- [Langfuse](https://techagents.online/tools/langfuse): Open-source LLM tracing and evaluation. Pricing model: open-source.
- [LangGraph](https://techagents.online/tools/langgraph): Graph-structured agent runtime. Pricing model: open-source.
- [Ollama](https://techagents.online/tools/ollama): Run open-weight models locally. Pricing model: open-source.
- [Pinecone](https://techagents.online/tools/pinecone): Managed vector database. Pricing model: freemium.
- [Braintrust](https://techagents.online/tools/braintrust): Evaluation and experiment tracking for AI. Pricing model: freemium.
- [Claude API](https://techagents.online/tools/claude-api): Anthropic’s model API for long-context and agentic work. Pricing model: paid.
- [Continue](https://techagents.online/tools/continue): Open-source assistant you configure yourself. Pricing model: open-source.
- [CrewAI](https://techagents.online/tools/crewai): Role-based multi-agent orchestration. Pricing model: open-source.
- [Hugging Face](https://techagents.online/tools/hugging-face): Model, dataset and inference hub. Pricing model: freemium.
- [Inngest](https://techagents.online/tools/inngest): Event-driven durable functions. Pricing model: freemium.
- [LangSmith](https://techagents.online/tools/langsmith): Tracing and evals with deep LangChain support. Pricing model: freemium.
- [LlamaIndex](https://techagents.online/tools/llamaindex): Data framework for retrieval over your own content. Pricing model: open-source.
- [Modal](https://techagents.online/tools/modal): Serverless GPU compute defined in Python. Pricing model: freemium.
- [n8n](https://techagents.online/tools/n8n): Workflow automation with AI nodes. Pricing model: freemium.
- [Next.js](https://techagents.online/tools/nextjs): React framework with server rendering built in. Pricing model: open-source.
- [OpenAI Platform](https://techagents.online/tools/openai-platform): Hosted model APIs across text, image and audio. Pricing model: paid.
- [OpenRouter](https://techagents.online/tools/openrouter): One endpoint in front of many model providers. Pricing model: paid.
- [pgvector](https://techagents.online/tools/pgvector): Vector similarity search inside PostgreSQL. Pricing model: open-source.
- [Pydantic AI](https://techagents.online/tools/pydantic-ai): Typed agents with validated outputs. Pricing model: open-source.
- [Qdrant](https://techagents.online/tools/qdrant): Vector search engine with first-class filtering. Pricing model: open-source.
- [Replicate](https://techagents.online/tools/replicate): Run and fine-tune models behind an API. Pricing model: paid.
- [Supabase](https://techagents.online/tools/supabase): Postgres backend with auth, storage and edge functions. Pricing model: open-source.
- [Temporal](https://techagents.online/tools/temporal): Durable execution for long-running workflows. Pricing model: open-source.
- [Vercel](https://techagents.online/tools/vercel): Deployment platform for frontend and AI applications. Pricing model: freemium.
- [vLLM](https://techagents.online/tools/vllm): High-throughput inference server for open models. Pricing model: open-source.
- [Weaviate](https://techagents.online/tools/weaviate): Open-source vector database with hybrid search. Pricing model: open-source.
- [Windsurf](https://techagents.online/tools/windsurf): Agentic IDE with a persistent task context. Pricing model: freemium.
- [Zed](https://techagents.online/tools/zed): High-performance editor with a bring-your-own-model assistant. Pricing model: open-source.

## Comparisons

- [Claude vs GPT](https://techagents.online/compare/claude-vs-gpt): An architecture-level comparison of Claude and GPT for developers: modality coverage, tool use, long-context behaviour, ecosystem support and cost control.
- [Gemini vs Claude](https://techagents.online/compare/gemini-vs-claude): How Gemini and Claude differ in practice: native multimodality, context handling, grounding, tool use, cloud integration and the trade-offs each choice carries.
- [Next.js vs React](https://techagents.online/compare/nextjs-vs-react): Next.js and React are not alternatives — one is built on the other. Here is the comparison people actually mean, and how to decide between the two setups.
- [PostgreSQL vs MongoDB](https://techagents.online/compare/postgresql-vs-mongodb): Relational and document databases compared on data modelling, transactions, query power, scaling and operations — with the false dichotomy addressed directly.
- [RAG vs Fine-Tuning](https://techagents.online/compare/rag-vs-fine-tuning): Retrieval adds knowledge, fine-tuning changes behaviour. A trade-off comparison covering cost, freshness, attribution, access control and failure modes.
- [Supabase vs Firebase](https://techagents.online/compare/supabase-vs-firebase): Relational versus document, SQL policies versus security rules, portability versus platform depth — how Supabase and Firebase actually differ in production.

## Categories

- [AI Agents](https://techagents.online/category/ai-agents): Autonomous and semi-autonomous AI systems that plan, call tools and act on real workflows. (2 articles)
- [Artificial Intelligence](https://techagents.online/category/artificial-intelligence): Large language models, generative systems and the research shaping applied AI. (1 article)
- [AI Development](https://techagents.online/category/ai-development): Building, shipping and operating AI-powered products with real engineering constraints. (3 articles)
- [AI Tools](https://techagents.online/category/ai-tools): Hands-on assessments of the models, IDEs, frameworks and platforms developers actually use. (1 article)
- [AI News](https://techagents.online/category/ai-news): Model launches, platform changes and industry shifts, with the engineering context attached. (no articles yet)
- [Web Development](https://techagents.online/category/web-development): Modern web engineering — rendering models, performance budgets and platform APIs. (no articles yet)
- [JavaScript](https://techagents.online/category/javascript): The language itself — runtime behaviour, async models, tooling and TypeScript at the edges. (no articles yet)
- [React](https://techagents.online/category/react): Server Components, rendering behaviour, state and the patterns that scale past a demo. (no articles yet)
- [Next.js](https://techagents.online/category/nextjs): App Router architecture, caching, data fetching and deployment on the modern Next.js stack. (1 article)
- [Python](https://techagents.online/category/python): The default language of the AI stack — from data pipelines to agent runtimes. (no articles yet)
- [APIs](https://techagents.online/category/apis): API design, protocols and the interfaces that let agents and services talk to each other. (1 article)
- [DevOps](https://techagents.online/category/devops): Pipelines, observability and the operational surface of shipping software continuously. (no articles yet)
- [Cybersecurity](https://techagents.online/category/cybersecurity): Application and AI security — prompt injection, supply chain, identity and defensive design. (1 article)
- [Cloud](https://techagents.online/category/cloud): Serverless, edge and the compute models behind AI-era infrastructure. (no articles yet)
- [Robotics](https://techagents.online/category/robotics): Embodied AI, perception and control systems where software meets the physical world. (no articles yet)
- [Startups](https://techagents.online/category/startups): How AI-native companies are built, funded, priced and differentiated. (no articles yet)
- [Tutorials](https://techagents.online/category/tutorials): Step-by-step technical builds you can follow end to end and run yourself. (1 article)
- [Reviews](https://techagents.online/category/reviews): Structured, criteria-based assessments of tools and platforms — methodology stated up front. (1 article)

## Agents

Seven specialised roles that describe what agentic software is actually being
asked to do, and together form the map of what this publication covers. Six
are editorial descriptions rather than software that runs here. The seventh,
the SEO Agent, is implemented in this codebase: it audits the corpus and
proposes changes that a person then reviews, and it has no write path to any
file.

- [Research Agent](https://techagents.online/agents/research-agent): Analyzing technical knowledge — Finds and summarizes technical information.
- [Coding Agent](https://techagents.online/agents/coding-agent): Reading and writing software — Helps developers understand and build software.
- [SEO Agent](https://techagents.online/agents/seo-agent): Auditing search visibility — Analyzes technical SEO and search visibility.
- [Content Agent](https://techagents.online/agents/content-agent): Planning editorial coverage — Assists with content research and planning.
- [Data Agent](https://techagents.online/agents/data-agent): Interrogating structured data — Analyzes structured and unstructured data.
- [Automation Agent](https://techagents.online/agents/automation-agent): Orchestrating workflows — Explores workflows and AI automation.
- [Security Agent](https://techagents.online/agents/security-agent): Probing for weaknesses — Reviews systems for security weaknesses.

## About

- [About Tech Agents](https://techagents.online/about): What we cover, how we source it, corrections policy and the editorial standards above.
- [Contact](https://techagents.online/contact): Tips, corrections and press. Corrections are made in place and the article re-dated.
- [Terms](https://techagents.online/terms): Who owns the content, how to quote it, and the licence on code samples.
- [Privacy](https://techagents.online/privacy): What the site collects, which is close to nothing, and what the newsletter stores.
- [Advertise](https://techagents.online/advertise): How sponsorship works and why it never buys a verdict.
- [Newsletter](https://techagents.online/newsletter): The Agent Brief — One issue every Tuesday. Unsubscribe in one click.
- [Resources](https://techagents.online/resources): Curated primary sources: specifications, provider documentation and reference implementations.
- [All categories](https://techagents.online/categories): Every section of the publication with its article count.
- [Article archive](https://techagents.online/blog): Every article, newest first.
- [RSS feed](https://techagents.online/rss.xml): Valid RSS 2.0. Standfirsts rather than full bodies, deliberately.
- [Sitemap](https://techagents.online/sitemap.xml): Every indexable URL with a lastmod drawn from real content dates.