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183 articles published

Building an AI Agent Marketing Content Pipeline: Research, Draft, and Publish

A practical guide to building a multi-step AI agent pipeline that handles the full marketing content lifecycle — from topic research and competitive analysis through drafting, SEO optimisation, and publishing to your CMS — with human review checkpoints that keep quality high without slowing things down.

Google ADK: Building Multi-Agent Systems With the Agent Development Kit

Google's Agent Development Kit is an open-source, code-first framework for building multi-agent AI systems in Python, Go, and Java. ADK 2.0 brings graph-based workflows, a built-in web UI for debugging, and native deployment to Vertex AI — making it a practical starting point for teams building production agent pipelines.

IBM watsonx Orchestrate: Enterprise AI Agents for Business Workflow Automation

IBM watsonx Orchestrate is IBM's enterprise AI agent platform for automating complex business workflows across HR, procurement, and customer service. This guide covers its architecture, 500+ pre-built skill integrations, multi-agent orchestration capabilities, and how it compares to Microsoft Copilot Studio and Salesforce Agentforce.

Temporal.io for AI Agents: Building Workflows That Survive Anything

Most AI agent failures happen not because the model gets it wrong, but because the infrastructure around it doesn't handle crashes, retries, and long-running state. Temporal solves this with durable execution — here's how it works and when to use it.

Agentless AI: When Simpler Pipelines Beat Full Agent Frameworks

Not every LLM workflow needs an agent framework. This guide examines when direct API calls, simple chains, and structured outputs outperform full-blown agent architectures — and how to identify the right tool for the job.

browser-use: AI Agents That Actually Control the Browser

browser-use is a Python library that gives AI agents structured access to real browser DOM state, enabling reliable web automation beyond what screenshot-based agents or Playwright scripts can handle. This guide covers the architecture, use cases, and limitations.

Building Deep Research Agents: Patterns for Autonomous Multi-Step Web Research

Deep research agents go beyond single-query RAG by iteratively refining search strategies, evaluating source quality, and synthesising findings across dozens of sources. This guide covers the architectural patterns, prompt strategies, and tooling behind production-grade autonomous research agents.

LangGraph Studio: Visual Debugging for Production AI Agent Graphs

LangGraph Studio gives developers a visual IDE for inspecting, stepping through, and editing AI agent graphs at runtime. Here's how it works, what it's good for, and how to set it up alongside your existing LangGraph workflows.

Building an AI Agent for DevOps Incident Response

How to build a production-ready AI agent that detects anomalies, executes runbooks, and drafts postmortems — using LangGraph, PagerDuty, and your existing observability stack.

Groq API for AI Agent Pipelines: Ultra-Fast Inference Without the Wait

Groq's LPU-based inference delivers 800+ tokens per second on Llama 3.3 70B and 1,200+ on smaller models — a step change in latency that changes how you architect multi-step agent loops. Here's what it's good for, where it fits in a production pipeline, and how to start using it.

AI Agents for Contract Review and Legal Document Automation

AI agents can now handle meaningful chunks of legal document work — clause extraction, risk flagging, obligation tracking, and comparison across versions. This guide covers practical architectures for building contract review workflows that actually work in production.

Gemini CLI: Google's Open-Source AI Coding Agent in the Terminal

Google's Gemini CLI brings Gemini 2.5 Pro to your terminal as an open-source AI coding agent. Free tier included, MCP server support, and a 1M token context window. Here's what it offers and how it fits into developer workflows alongside Claude Code and Codex.

AI Agent Error Recovery and Graceful Degradation Patterns

Production AI agents fail in ways that simple try/catch blocks can't handle — tool timeouts, partial tool results, context window saturation, and cascading LLM errors. This guide covers the retry strategies, fallback chains, circuit breakers, and checkpoint patterns that keep agentic workflows running when things go wrong.

Building an AI Agent to Tame Your Inbox in 2026

Email agents have moved well past suggested replies. The useful ones in 2026 triage, draft, trigger workflows, and know when to hand back to you — here's how to set one up that actually helps.

AI Agents for Knowledge Management: Beyond the Company Wiki

Company wikis are full of outdated information nobody trusts. AI agents are offering a different model — dynamically maintained, searchable, and connected to the systems where work actually happens. Here's how teams are making it work.

Hatchet: Open-Source Durable Task Queue for AI Agent Workflows

Hatchet is an open-source durable task queue built on Postgres that gives AI agent pipelines step-level persistence, automatic retries, fan-out, and concurrency controls — without managing Redis, Celery, or a separate queue infrastructure.

Using Anthropic's Files API for AI Agent Document Workflows

Anthropic's Files API lets you upload documents once and reference them across multiple agent calls by file ID. For agents working with reports, contracts, or knowledge bases, this reduces token usage and latency on repeat calls — without a vector database.

browser-use: Open-Source Web Browser Automation for AI Agents

browser-use is a Python library that connects AI agents to a real Playwright-controlled browser, letting them navigate pages, click buttons, fill forms, and extract data just by reading what's on screen. Here's how it works and how to build with it.

Instructor: Extracting Structured Data from LLMs in Python

Instructor is a Python library that patches OpenAI-compatible clients to return validated Pydantic models instead of raw text. This guide covers how it works, common patterns, and how it compares to native structured output APIs.

Building AI Research Agents with the Perplexity Sonar API

Perplexity's Sonar API gives your AI agents real-time web search with citations built in. Here's how to integrate it into research workflows, compare it to native LLM browsing, and decide when it's the right tool.

LlamaIndex Workflows: Stateful, Event-Driven Agent Pipelines in Python

LlamaIndex Workflows provide a structured, event-driven architecture for multi-step agent pipelines with explicit state management, typed events, and async-native execution — filling the gap between raw agent loops and full orchestration frameworks.

DSPy: Automated Prompt Programming for Production AI Systems

DSPy from Stanford replaces manually written prompts with compiled, optimised programs. Instead of tuning prompt strings by hand, you define the task signature and let DSPy's optimisers find the most effective prompting strategy using your examples and a metric. Here's how to use it in production.

OpenHands: The Open-Source AI Software Engineer for Agent Workflows

OpenHands (formerly OpenDevin) is a self-hostable AI agent that can write code, run tests, browse documentation, and open pull requests — now scoring 68.4% on SWE-bench Verified. Here's how to integrate it into your development workflow.

Multi-Agent Debate: Adversarial Patterns That Improve LLM Output Quality

Single LLM calls produce confident-sounding answers regardless of correctness. Multi-agent debate — having agents argue positions and critique each other — measurably improves accuracy, catches errors, and surfaces assumptions. Here's how to implement it in LangGraph, CrewAI, and the OpenAI Agents SDK.

Exa AI: Neural Search for AI Agent Research Workflows

Exa's neural search API finds the web's most relevant content using meaning rather than keyword matching. Here's how it compares to traditional search APIs, when it wins, and how to integrate it into your AI agent research pipelines in 2026.

AI Agent Identity: Who Is Your Agent and What Can It Do?

AI agents aren't users and they aren't service accounts. Traditional IAM doesn't fit them well, and the permission gaps create real security risks. Here's how to think about identity and access for autonomous AI systems.

AI Agent Rate Limiting and Cost Control in Production 2026

Production AI agents can burn through API budgets in minutes if left unchecked. Here's how to implement rate limiting, cost caps, and throttling patterns that keep agents reliable without surprises on your bill.

Toolhouse.ai: Managed MCP Tool Hosting for AI Agents

Toolhouse.ai provides a hosted tool store built on the Model Context Protocol — so your agents get web search, code execution, email, and dozens of other capabilities without you running any MCP servers. Here's how it works and when it makes sense.

LiteLLM Proxy: Unified AI Gateway for Multi-Model Agent Pipelines

LiteLLM Proxy acts as a local gateway that routes LLM calls to 100+ models behind a consistent OpenAI-compatible API. Here's how to use it to simplify multi-provider agent architectures, reduce costs, and add observability without rewriting your agent code.

GraphRAG: When Standard Retrieval Isn't Enough for Complex Queries

Microsoft's GraphRAG extends retrieval-augmented generation by building knowledge graphs over document collections, enabling AI agents to answer questions that require connecting multiple documents — not just finding relevant chunks.

Microsoft Semantic Kernel: The Enterprise AI Agent SDK That's Actually Built for Production

Semantic Kernel is Microsoft's open-source SDK for building AI agents and copilots in C#, Python, and Java. It handles plugin registration, memory integration, and multi-step planning — and it's the framework under the hood in Microsoft Copilot. Here's how it works and when it makes sense over LangChain, PydanticAI, or rolling your own.

Vercel AI SDK: The Practical Guide to Building AI Apps and Agents in TypeScript

The Vercel AI SDK is now one of the most widely used frameworks for building AI applications in TypeScript — covering streaming responses, tool calling, structured outputs, and multi-step agents across any model provider. Here's what it does well, where it fits, and how to use it for agent workflows.

Building Local AI Agent Workflows with Ollama and Open-Source LLMs

Running AI agents on local hardware with Ollama and open-source models like Llama 3.3 and Qwen2.5 keeps your data private, eliminates API costs, and works offline. Here's how to build practical agent workflows that run entirely on your own machine.

Webhook-Driven AI Agents: Building Event-Triggered Automation Pipelines

Polling loops waste compute and introduce latency. Webhook-driven agents wake on real events — a new Stripe payment, a GitHub PR, an inbound email — and execute immediately. This guide covers the architecture for event-triggered agent pipelines, how to handle webhook delivery guarantees, and patterns for keeping agents reliable when events arrive out of order or at scale.

The Four Multi-Agent Orchestration Patterns You Need in Production

Orchestration is replacing prompt engineering as the core AI skill. This guide breaks down the four production patterns — chaining, fan-out, routing, and hierarchical orchestration — and explains when to use each so you can build systems that actually hold up under real load.

PydanticAI: Building Reliable, Type-Safe AI Agents in Python

PydanticAI brings Pydantic's structured validation approach to AI agent development. Here's why its type-safe design makes it a strong choice for production agent systems where hallucination-prone outputs are a real cost.