Harness Engineering Part 7: How AI Agents Are Built to Remember

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A new installment in the 10-part Harness Engineering series explains how to give AI agents persistent memory across sessions and within tasks. The article distinguishes between short-term memory, which keeps an agent coherent during a single task, and long-term memory, which retains information across separate sessions. Without an explicit memory layer, language models start each interaction with no recollection of past exchanges, making them feel like strangers to returning users. The piece outlines three core design decisions for building a real memory system: choosing a memory flavor, setting write triggers, and implementing bounded retrieval. The series targets developers building production-grade agentic systems from the ground up.

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Zenoh put/get race condition in Elixir causes stale reads 3.9% of the time

A developer experimenting with Zenoh's put/get storage feature via Elixir bindings (Zenohex) discovered a read-after-write race condition where a get issued immediately after a put occasionally returns a stale value. In a 2,000-iteration test loop, roughly 78 reads (3.9%) retrieved outdated data, though querying again just ~1ms later consistently returned the correct value. The root cause is an asymmetry in Zenoh's design: put is fire-and-forget at the NIF level, while get is a true request/response operation, creating a brief window before a write becomes visible. The developer built a workaround called ZenohAckPut — an application-level wrapper that confirms a write by re-querying until the expected value is returned — which eliminated stale reads entirely in the same test. The fix is available as an open-source Elixir module on GitHub, and the author notes the underlying gap remains unresolved upstream in Zenoh itself.

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ProgrammingDEV Community ·

BlocSignal Offers Clean One-Shot UI Side Effects Without Extra Flutter Packages

Flutter developers commonly face a 'Sticky State Dilemma' where transient UI events like snackbars or dialogs re-trigger unintentionally after widget tree rebuilds caused by screen rotation or keyboard activity. Traditional BLoC-based workarounds — such as emitting reset states or embedding UI flags inside domain state classes — introduce rebuild overhead and pollute business logic. A third-party package called bloc_presentation addressed this by adding a separate broadcast stream to BLoCs, but it adds versioning and dependency management costs. BlocSignal tackles the problem differently by making state propagation synchronous, settling updates within the same frame and eliminating the need for secondary presentation streams in many cases. This allows one-shot side effects like navigation or dialogs to be handled directly in UI callbacks, keeping domain state clean and free of ephemeral UI concerns.

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ProgrammingDEV Community ·

Basehim: Open-Source, API-First PHP CMS Built for AI-Assisted Development

Basehim is a newly launched open-source, modular, API-first PHP CMS designed for developers, AI-assisted workflows, and AI agents. Unlike many modern tools that assume complex infrastructure, Basehim runs on standard PHP and MySQL environments, including shared hosting via cPanel or Plesk, with no Composer, build pipeline, or containerization required. The CMS is built around three core principles: API-first design, modularity, and AI-readiness, exposing a REST API for resources such as posts, pages, media, and settings. It supports multiple authentication methods — API keys, JWT, and OAuth 2.1 — tailored to different callers including scripts, applications, and AI agents. A built-in Model Context Protocol (MCP) server is also included, providing a standardized interface for AI applications to interact with the CMS securely.

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ProgrammingDEV Community ·

Harness Engineering Part 8: Why Observability Is Essential for AI Agents

Part 8 of the 10-part Harness Engineering series focuses on observability, the final component of a production-ready agentic system. Observability encompasses full-fidelity logs of model calls, traces of tool executions, latency and token metrics, and fixed evaluations to detect regressions. The author argues that without this instrumentation, engineers cannot determine what an agent did, why it failed, or whether it is improving over time. AI agents are particularly difficult to operate because they are non-deterministic, involve multiple steps, and can run for extended periods without direct supervision. The piece emphasizes that building an agent and being able to reliably operate one are fundamentally different achievements.

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