What's Worth Knowing In AI Right Now? (with Henry Garner)

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Mar 26, 2026

Henry Garner, CTO of Juxt and creator of the Juxt AI Radar, focuses on AI-assisted software development and the Allium behavioral spec language. He discusses the AI tooling adoption arc, orchestration frameworks versus agents, neurosymbolic approaches and the surprising return of Prolog. He also covers Allium’s role in giving LLMs structure and practical trade-offs in fine-tuning, observability and running models.

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INSIGHT

Orchestration Needs Design Constraints

Orchestration trades runtime flexibility for predictability; precise frameworks (LangChain/LangGraph) add confidence while open agent stacks (Gastown) risk chaos.
Henry prefers specifying intent then fanning out parallel agents under constrained design.

INSIGHT

Pair LLMs With Symbolic Reasoners

Neurosymbolic systems pair LLMs' fuzzy pattern reasoning with symbolic tools for audited correctness.
Henry suggests injecting Prolog or helper functions so rigorous steps can be executed and audited alongside LLM translation.

ADVICE

Fine‑Tune Only With Large Proprietary Data

Prefer frontier models plus RAG pipelines for most use cases; fine‑tune only when you have large proprietary labeled datasets.
Henry used fine‑tuning for regulatory text classification where hundreds of thousands of examples improved performance.

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AI is changing the way we all build software — that much seems clear. But the landscape is moving so fast that even the people paid to keep up are struggling. MCP or skills? Fine-tune or just prompt? LangChain or let a thousand agents loose? With almost 70 competing technologies and a shelf life of maybe six months on any advice, how do you figure out what's actually worth your time?

Henry Garner is CTO of JUXT, a consultancy with about 150 senior engineers working at the coalface of AI-assisted development, including building AI platforms for tier-one banks. JUXT publishes a quarterly AI Radar — 68 technologies rated and reviewed — and Henry's been watching his own team go through the full adoption arc, from "spicy autocomplete" skepticism through to building Byzantine-fault-tolerant distributed systems over a weekend with Claude. Along the way we cover MCP vs skills, Conway's Law for LLMs, neurosymbolic AI and the unexpected return of Prolog, the "Ralph Wiggum loop" for getting agents to converge on correct implementations, and Allium — a new behavioral specification language Henry's co-authored that sits between human prose and TLA+, aiming to give LLMs just enough structure to pin down what a system should do without falling into waterfall thinking.

If you're trying to make sense of the AI tooling landscape, or you've hit that wall where your agents keep drifting away from what you actually wanted, Henry's thesis — velocity through clarity of intent — might well help out yours.

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JUXT: https://www.juxt.pro/

JUXT AI Radar: https://www.juxt.pro/ai-radar/

Allium on GitHub: https://github.com/juxt/allium

Allium Documentation: https://juxt.github.io/allium/

Composition at a Distance (Henry's blog post): https://www.juxt.pro/blog/composition-at-a-distance/

A New Vocabulary for an Old Problem (Henry's blog post): https://www.juxt.pro/blog/new-vocabulary-for-an-old-problem/

Model Context Protocol (MCP): https://modelcontextprotocol.io/

LangChain: https://www.langchain.com/

LangGraph: https://www.langchain.com/langgraph

Gas Town (Steve Yegge): https://github.com/steveyegge/gastown

Kiro (spec-driven AI IDE): https://kiro.dev/

Phoenix (LLM observability): https://github.com/Arize-ai/phoenix

Temporal: https://temporal.io/

Taalas (LLM-on-a-chip): https://taalas.com/

Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.social

Kris on Mastodon: http://mastodon.social/@krisajenkins

Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/