Things we learned
by running agents.
Engineering posts on durable execution, policy, audit, memory, and the boring reliability work that makes AI agents survive past the demo.
- 01
I tried to delete a database with an AI agent. The runtime said no.
JamJet 0.8.1 (Python) and @jamjet/cloud 0.2.2 (TypeScript) ship a runtime safety layer that intercepts an agent's tool calls before the tool function is invoked — and the four zero-setup demos prove the path.
Read → - 02
Every AI toolchain is inventing its own safety layer. We shipped one that works across all of them.
JamJet shipped a portable policy layer that runs the same safety rules across Claude Code hooks, OpenAI Agents SDK guardrails, MCP stdio traffic, and the JamJet Python/TS SDKs. One policy file. One audit trail.
Read → - 03
When AI Deletes the Database
From PocketOS to Replit, AI agents are wiping production databases. Why this is a runtime problem -- not a model problem -- and the architecture pattern that prevents it.
Read → - 04
Engram on LongMemEval: What Worked, What Didn't, What We Learned
We added 8 retrieval-and-reading techniques to Engram and benchmarked each independently against LongMemEval-S. Three shipped, five didn't. The negative results turned out to be the most useful part.
Read → - 05
Your AI Agents Won't Survive an Audit
89% of enterprise AI agents never reach production. The EU AI Act is enforceable in August. Here's what production safety actually requires — and why most agent frameworks aren't ready.
Read → - 06
Zero-Sidecar Durable AI Agents in Java
Kill your agent. Restart it. It remembers everything. The JamJet Java Runtime embeds durable execution directly in your JVM — no Docker, no sidecar, no REST overhead.
Read → - 07
How to Choose an AI Agent Framework in 2026
LangGraph, CrewAI, AutoGen, Google ADK, JamJet — the landscape is crowded. Here is a practical decision framework for picking the right one.
Read → - 08
Why Your AI Agents Need Observability — and What to Measure
You would not deploy a microservice without metrics and tracing. Why are you deploying AI agents blind? Here is what to measure and how.
Read → - 09
Google ADK vs JamJet: Building a Claims Processing Agent
We built the same insurance claims agent in both frameworks. One crashes and loses everything. The other picks up exactly where it left off.
Read → - 10
Getting Started with MCP: Connect AI Agents to Any Tool
Model Context Protocol is becoming the USB-C of AI agents. Here is how to connect your agents to databases, APIs, and file systems — with working code.
Read → - 11
Engram: A Memory Layer for AI Agents That Actually Works
One cargo install. Zero infrastructure. Your agents remember everything — with temporal knowledge graphs, semantic search, and MCP-native tools.
Read → - 12
The State of Memory in Java AI Agents (April 2026)
A tour of every option Java developers have for adding persistent memory to AI agents — and why most of them stop at chat history.
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The Companies Quietly Replacing Entire Workflows with AI Agents — While You're Still Debating Prompts
While most teams argue about prompt engineering, early movers are shipping autonomous agent workflows that handle claims, onboarding, and due diligence end-to-end. Here's what they know that you don't.
Read → - 14
Akka Agents vs JamJet: Actor Model or Agent-Native Runtime?
Two production-grade approaches to AI agents on the JVM. Akka adapted 20 years of actor infrastructure. JamJet was purpose-built from day one. An honest architectural comparison with code, diagrams, and a decision matrix.
Read → - 15
JamJet Spring Boot Starter — Production-Grade Agent Runtime for Spring AI
Add one dependency to your Spring Boot application. Get crash recovery, audit trails, replay testing, and human-in-the-loop for every Spring AI agent call. JamJet brings its full agent runtime — strategies, multi-agent coordination, MCP, A2A, eval harness — to the Spring ecosystem.
Read → - 16
Every Major AI Agent Failure Has the Same Root Cause
Klarna, Air Canada, DPD — sourced post-mortems of real AI agent failures. The pattern is always the same: prototype infrastructure in production. Named companies, real timelines, avoidable lessons.
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AI Agents Need Their Spring Moment — It Starts with the Runtime
Spring transformed how Java built enterprise apps. AI agents need the same transformation — not another framework, but a production runtime. A sourced comparison of every major JVM AI framework and where the gap remains.
Read → - 18
What Your Competitors Are Already Doing With AI Agents
Named companies, real metrics, sourced data. How finance, legal, support, and insurance deploy AI agents in production — and what it means if you haven't started.
Read → - 19
Why AI Agents Are the Next Competitive Advantage — and What Leaders Need to Know
What AI agents mean for business leaders: faster decisions, better scale, and a new operating model. No code, no jargon — just the strategic case.
Read → - 20
What's New: Incremental Streaming, LLM Tiebreaker, and Reasoning Modes
True incremental NDJSON streaming for agent tools, async LLM tiebreaker for coordinator routing, and reasoning mode scoring for Agent Cards.
Read → - 21
Why We Built JamJet
The demo-to-production gap in AI agents is real. Here is why we built a new runtime instead of reaching for another framework.
Read → - 22
Building a multi-agent wealth advisor with JamJet
Four specialist AI agents — risk profiler, market analyst, tax strategist, portfolio architect — collaborate through a durable workflow to produce investment recommendations. A deep dive into the architecture, with a side-by-side comparison to Google ADK.
Read → - 23
OAuth delegation and federation auth for AI agents
RFC 8693 token exchange, scope narrowing, per-step scoping, mTLS federation — how JamJet ensures agents never exceed the permissions they were granted.
Read → - 24
Phase 4: Enterprise security for production agents
Multi-tenant isolation, PII redaction, OAuth delegation, mTLS federation — the enterprise layer that lets agents handle real data in real organizations.
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Data governance for AI agents: PII, redaction, and retention
How JamJet's data policy engine handles PII detection, automatic redaction, and time-based retention — enforced by the Rust runtime, not by convention.
Read → - 26
Migrating from LangGraph to JamJet: what actually changes
A side-by-side walkthrough of the same workflow in LangGraph and JamJet — what maps across, what disappears, and what you gain.
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Testing AI agents like software
Most teams test their agents by running them manually and eyeballing the output. There is a better way — and it fits in a CI pipeline.
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Phase 3: Eval Harness, Project Templates, and the Path to Trustworthy Agents
Shipping the eval harness, four built-in project templates, and why testing your agents the same way you test software is the only path forward.
Read → - 29
Building a self-evaluating AI agent in 50 lines
Draft, judge, retry. A workflow that scores its own output and loops until it is good enough — or gives up gracefully.
Read → - 30
Why I built JamJet's runtime in Rust
Not a trendy choice. A conviction-based one. Here is what it cost, what it taught me, and why I would do it again.
Read → - 31
Announcing JamJet: The Agent-Native Runtime
We built the runtime we wished existed for AI agents — durable, composable, and built for production from day one.
Read →