Engineering, architecture, and strategy for durable AI agents.
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.
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.
What AI agents mean for business leaders: faster decisions, better scale, and a new operating model. No code, no jargon — just the strategic case.
The demo-to-production gap in AI agents is real. Here is why we built a new runtime instead of reaching for another framework.
Not a trendy choice. A conviction-based one. Here is what it cost, what it taught me, and why I would do it again.
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.
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.
A side-by-side walkthrough of the same workflow in LangGraph and JamJet — what maps across, what disappears, and what you gain.
Draft, judge, retry. A workflow that scores its own output and loops until it is good enough — or gives up gracefully.
RFC 8693 token exchange, scope narrowing, per-step scoping, mTLS federation — how JamJet ensures agents never exceed the permissions they were granted.
Multi-tenant isolation, PII redaction, OAuth delegation, mTLS federation — the enterprise layer that lets agents handle real data in real organizations.
How JamJet's data policy engine handles PII detection, automatic redaction, and time-based retention — enforced by the Rust runtime, not by convention.
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.
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.
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.
True incremental NDJSON streaming for agent tools, async LLM tiebreaker for coordinator routing, and reasoning mode scoring for Agent Cards.
We built the runtime we wished existed for AI agents — durable, composable, and built for production from day one.