Alternatives
Products that do what Kybernis – Prevent AI agents from executing the same action twice does
AI agents increasingly execute real system actions: issuing refunds, modifying databases, deploying infrastructure, calling external APIs. Because agents retry steps, re-plan tasks, and run asynchronously, the same action can sometimes execute more than once. In production systems this can cause duplicate payouts, repeated mutations, or inconsistent state. Kybernis is a reliability layer that sits at the execution boundary of agent systems. When an agent calls a tool: 1. execution intent is captured 2. the action is recorded in an execution ledger 3. idempotency guarantees are attached 4.…
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Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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We built an open sourced coordination layer for AI agents working on the same repository. Detects work duplication and design conflicts early
9d ago · twing.dev
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AI agents accumulate stale tool results — file reads, web fetches, bash outputs — in their context window. Every one sits there for the entire conversation, consuming tokens and degrading quality. The standard fix is auto-compaction: wait until full, then drop content indiscriminately. Context Surgeon gives the agent three operations — evict, replace, and restore — so it can manage its own context. It works as a transparent local proxy that intercepts API requests, assigns IDs to content blocks, and applies eviction directives before forwarding. The agent calls the tools via bash. The proxy…
Apr 2026 · github.com
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Agent runs often fail after expensive model calls and executing tools that have real-world side effects. This problem is made even worse by how common it is to deploy agents to serverless environments. When your agent dies, it needs to be restarted, but doing so safely isn't easy and everyone building agents has to solve this same problem of durability. The stack you're running probably already has half of what you need for durable execution already though, ie, a queue or job runner that can invoke work at least once. kassette gives you the other half by journaling completed steps to object…
Jul 2026 · github.com
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Jun 2026 · riskkernel.com
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Hey HN, Most AI “agents” I’ve tried are basically chatbots with amnesia — they forget everything the moment you close the tab and can’t do anything unless you’re sitting there watching them. I wanted real AI coworkers that just… work. So I built Computer Agents (aiOS). Every agent you create gets its own isolated computer in the cloud — complete with persistent memory, a real file system, code execution environment (with automatic dependency management), and the ability to run scheduled or webhook-triggered tasks 24/7. You give it a goal (“research this market and email me a report…
Mar 2026 · computer-agents.com
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We combined Stanford's ACE (agents learning from execution feedback) with the Reflective Language Model pattern. Instead of reading traces in a single pass, an LLM writes and runs Python in a sandbox to programmatically explore them - finding cross-trace patterns that single-pass analysis misses. The framework achieved 2x consistency improvement on τ2-bench.
Mar 2026 · github.com
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Most multi-agent systems fail the same way: agents drift apart across handoffs. By turn 3 they are working in different realities. By turn 5 they are repeating each other's mistakes and calling it parallelism. WUPHF is an open-source local-first office where AI coworkers run on your laptop, around a shared markdown + git LLM wiki the agents build. The wiki is the collective memory. The office around it keeps the team on the same shared context across thousands of handoffs. What actually stops drift is not the wiki. It is the agents reviewing each other's work. The CRO catching the CMO's…
May 2026 · wuphf.team
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Apr 2026 · github.com
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Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…
Feb 2026 · limits.dev
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We build collaboration SDKs at Velt (YC W22). Comments, presence, real-time editing (CRDT), recording, notifications. A pattern we keep seeing: products add AI agents that write, edit, and approve things. Human actions get logged. Agent actions don't. Same workflow, different accountability. We shipped Activity Logs to fix this. Same record for humans and AI agents. Immutable by default. Auto-captures collaboration events, plus createActivity() for your own. Curious how others are handling this.
Apr 2026 · velt.dev
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AGENTS.lock keeps AI agent skills, instructions, and MCP servers in sync across Claude, Codex, Gemini, and Copilot CLIs using a single TOML lockfile as the source of truth. Instead of manually copying skills and configs between tools, you declare everything once in AGENTS.lock and run `al sync`. GitHub: https://github.com/luml-ai/AGENTS.lock
Jan 2026 · github.com
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hi all. i’ve been shipping a small open project that tries to answer that question with evidence, not vibes. in 70 days it reached \~800 stars. the core claim is simple: many AI failures are not noise. they repeat because the geometry and ordering underneath are stable. if so, we should be able to name each failure mode, set acceptance targets, and stop shipping the same bug twice. ### what it is * a compact Problem Map of 16 reproducible failure modes in RAG and agents. * each item has a minimal fix and measurable gates. examples: * Semantic ≠ Embedding: metric and normalization mismatch.…
2025 · github.com
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