Single-agent systems peaked with bounded tasks and one prompt. Production moved on: organizations now run double-digit agent counts, and a large share of multi-agent pilots die within months of deployment. The killer isn’t the idea — it’s picking the wrong coordination topology, or the right one without knowing its failure modes.
Open WebUI brings the ChatGPT experience onto your own infrastructure: Ollama plus any OpenAI-compatible backend behind a modern chat UI with RAG document chat, multi-user auth, voice, prompt tooling, and model management. Privacy-first, offline-capable, and scalable from laptop Docker runs to Kubernetes fleets.
Ollama is the easiest way to run a local model, until the experiment becomes a shared service. Requests queue, latency wobbles, prefixes recompute, and one GPU stops being enough. vLLM answers exactly those problems — but migration is a trade, not an upgrade: simplicity for scheduling, memory control, parallelism, and production operations.
Three skill frameworks promise to make coding agents write better code: an autonomous pipeline, a full-lifecycle system with adversarial guards, and a requirements-first toolkit. Their repos differ in philosophy — and a measured same-feature bake-off puts numbers on the tradeoffs: 78 minutes and $34 against 148 minutes and $92.
Spec-driven development works when the specification is a workflow, not a document filed away after kickoff. The point is a sequence of reviewable artifacts — requirements, design, tasks, implementation, validation — each reducing ambiguity before anyone, human or agent, changes production code.
Most coding agents keep getting bigger: planning modes, subagents, permission layers, IDE integrations, background workers. Pi walks the other way. Four default tools — read, write, edit, bash — with everything interesting left to extensions, skills, packages, and your own workflow. It is less a finished product than an editable harness, and that is exactly the point.
Claude Code isn’t autocomplete with better marketing — it’s a terminal-native junior engineer that needs review. This guide covers install, settings layering, self-hosted Ollama and llama.cpp backends, pricing rails, and troubleshooting.
Prompt injection gets the attention, but it stops being the whole problem once agents call tools and delegate work. Identity, authorization, delegation limits, and audit trails become first-class architecture — not optional hardening.
A spec nobody re-checks against the running system is worse than no spec: reviewers trust the document instead of the diff while the agent follows whatever the code actually does. Agents regenerate code fast, and each regeneration is a fresh chance for spec and implementation to diverge. The fix is a small enforceable link between requirement, design decision, tests, and the commits that changed them.
AI-generated code is cheap; judgment is scarce. When models write the code, nobody remembers why PostgreSQL beat DynamoDB, why AI emails must stay drafts, or why suggestions live in a side panel. Decision records — short, version-controlled documents capturing what was decided, why, and what was rejected — turn that judgment into durable project memory both humans and agents can read.