21 posts tagged with "Agent"

Deploying code to a major managed host like Pantheon or Acquia is relatively easy. Ensuring that your deployment didn't silently break component rendering across 50 regional sub-sites? That is significantly harder.

Manual QA represents an escalating bottleneck in enterprise CI/CD pipelines.

Enterprise Agile environments are undeniably slow. Developers easily spend 30% of their day just reading Jira tickets, interpreting scattered requirements, searching for the right database dumps, and manually spinning up Lando or Docker environments before writing a single line of code.

Your deploy was fine. Your WAF rule update was also fine. Both hitting the same service within fifteen minutes at 2 a.m.? That is where the outage lives, and your single-metric dashboards will smile green the entire time. Cloudflare wrote an entire postmortem about this blind spot — stacked low-signal anomalies that every alert evaluates in isolation and nobody evaluates together — so I turned it into an enforceable playbook before the next on-call learns the lesson the hard way.

SearXNG in Drupal AI assistants matters because it gives you controllable, inspectable retrieval without funneling your org's prompts and context into yet another black-box "trust us" API.

I needed to get data out of Jira. Not just the title, but the full description, comments, and all attachments, packaged neatly for use in other scripts. The official way involves wrestling with an API that feels like it was designed by a committee that never spoke to each other. The unofficial way involves paying $20/month for a SaaS tool that is just a glorified curl command wrapped in a pretty dashboard. I chose the third way: build it myself.

Every AI coding tool has its own config format for MCP servers. Claude uses JSON, Codex uses TOML, Gemini uses a different JSON schema. Setting up the same 18 servers across all three means editing three files, remembering three formats, and hoping you didn't typo a credential. I built mcp-web-setup to do it once.

LLM prompts are full of repeated English grammar. Every "If a module exists, then recommend it. Do NOT reinvent the wheel." burns tokens on words the model already understands. I built CPF (Compact Prompt Format) to replace that grammar with operators and abbreviations that LLMs decode natively — cutting token costs by 30-50% with zero runtime dependencies.

I had two collections of plain arrays and needed to associate them relationally, but I was not working with Eloquent models. Laravel's Collection class is powerful, but it has no built-in way to express a one-to-many relationship between two arbitrary datasets. I built laravel-collection-has-many to fix that.

Gemini Ollama CLI Bridge chains a local Ollama model with Google's Gemini into a two-stage code analysis pipeline. The first version piped data to Gemini through a shell subprocess -- fragile, hard to test, and impossible to cache. This upgrade replaces the shell integration with the google-generativeai Python SDK, adds result caching, and grows the test suite from 4 tests to 22.

The first version of Drupal AI Content Impact Analyzer was a minimal proof-of-concept: a single scoring function, no tests, no documentation worth mentioning. It proved the idea worked. It did not prove the idea was useful to editors who need to know exactly why a piece of content scores the way it does. The upgrade rewrites the core into a 6-dimension scoring engine that tells editors precisely what to fix.

The Drupal AI Hackathon: Play to Impact 2026, held in Brussels on January 27-28, was a pivotal moment for the Drupal AI Initiative. The focus was on practical, AI-driven solutions that enhance teamwork while upholding trust, governance, and human oversight.

The most compelling challenge: creating AI Agents for Content Creators — not simple content generation, but agentic workflows where AI acts as collaborator, researcher, or reviewer.

As AI agents become part of the Drupal workflow, you need infrastructure that makes automation traceable, predictable, and composable. I built four small Drupal modules that together form an MCP Toolkit for agent-driven site management.

The first version of drupal-entity-reference-integrity could scan a Drupal site and report broken entity references. That was the easy part. The hard part -- actually fixing them at scale without taking the site down -- was missing. This upgrade adds auto-fix mode, batch processing, and the test coverage needed to trust both in production.

I built this proof-of-concept to explore how a codex-style agent can assist with routine Drupal maintenance tasks -- dependency updates, configuration checks, and basic cleanup. The kind of work that is constant, easy to overlook, and expensive to do manually at scale.

The original POC had three actions -- create a node, read a node, list nodes. Enough to prove the concept, not enough to be useful. A real content operations agent needs to modify, delete, query users, browse taxonomies, and handle media. It also needs to survive flaky networks without crashing.

I built drupal-content-audit as a lightweight way to inspect and report on content in a Drupal site. It focuses on surfacing what content exists and how it is distributed, giving a quick snapshot that is easy to share with stakeholders.

I built a DDEV add-on that runs Playwright checks against local MCP endpoints using internal DDEV DNS names like web. This solves flaky localhost-based testing and gives one command for repeatable MCP smoke tests.

I built drupal-ai-module-generator-deepseek-mcp to take the repetitive, error-prone parts of module setup -- info files, boilerplate, and consistent structure -- and make them fast and repeatable. It fits naturally into agent-driven workflows where you want consistent Drupal modules without losing time to manual setup.

I built drupal-ai-views-agent because Views is powerful but repetitive. Filters, relationships, display modes, and fields add up quickly. Describing what you want in plain language and getting a valid Views config back saves the clicking and lets teams focus on validating data requirements instead of wrestling with UI details.

The first version of drupal-claude-agent-sdk-runtime gave you session management and result objects, but tools were mocked. You could simulate agent workflows inside Drupal, but you could not actually execute anything. That ceiling had to go.