{"slug": "learning-infrastructure-for-ai-agents", "title": "Learning Infrastructure for AI Agents", "summary": "AgentLoop launches a runtime learning layer for production AI agents that captures human corrections as reusable memory, enabling agents to avoid repeating mistakes without retraining. The tool wraps existing OpenAI and Anthropic clients, logs every turn, and retrieves relevant past corrections before each response, targeting the challenge of maintaining agent performance at scale.", "body_md": "#\nAI agents that\n\ndon't repeat the same\n\n*mistake twice.*\n\nAgentLoop is a runtime learning layer for production AI agents. Human corrections become reusable memory — searched before every response, applied automatically, improving the agent without retraining.\n\n## A loop, not a *retraining run.*\n\nEvery response gets reviewed — by a human, a heuristic, or a downstream signal. Corrections become memory. Memory gets pulled into the next prompt. Three steps, looping forever, without touching weights.\n\n### Retrieve relevant facts\n\nBefore each LLM call, AgentLoop searches past corrections semantically and injects the most relevant ones into the prompt. Your agent now knows what it was wrong about yesterday.\n\n### Log every turn\n\nThe response is logged automatically — question, answer, model, signals. No glue code. Failed cases surface in a review queue, ranked by signal strength.\n\n### Correct & ship\n\nA reviewer writes the correct answer once. It's embedded, deduplicated, and instantly available to every future query — same shape, same wording, fixed for good.\n\n## Wrap your client. *That's it.*\n\nNo SDK overhaul. No prompt-engineering rewrite. AgentLoop's wrappers keep the OpenAI and Anthropic interfaces exactly as they are — and add memory retrieval and turn logging behind the scenes.\n\n``` python\nfrom openai import OpenAI\nfrom agentloop import AgentLoop\nfrom agentloop_openai import wrap_openai\n\n# 1. Wrap your existing OpenAI client.\nclient = wrap_openai(\n    OpenAI(),\n    loop=AgentLoop(api_key=\"ak_live_...\"),\n)\n\n# 2. Use it like normal. AgentLoop runs around it.\nresponse = client.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n        {\"role\": \"user\", \"content\": question},\n    ],\n    agentloop={\"user_id\": user.id},\n)\n```\n\n## Built for the messy middle, *not the demo.*\n\nMost agents look great on stage and break in production. The hard part isn't the first response — it's the thousandth, when the edge cases outnumber the happy path.\n\n### Stop hand-patching the system prompt\n\nEvery shipping team eventually maintains a 4,000-line system prompt full of \"always remember that…\" exceptions. AgentLoop replaces that with structured memory — searchable, deduplicated, editable, audited.\n\n### Close the feedback loop without a fine-tuning run\n\nSubject-matter experts write the fix once, in plain language. It applies to every future user, every future session, in seconds — not the next training cycle. The dashboard surfaces what reviewers actually fixed, not what models reported.\n\n### Python and JavaScript, real parity\n\nThe Python and JS SDKs produce byte-identical HMAC signatures. Feedback URLs signed in one validate in the other. Both languages hit the same backend, so behavior is consistent regardless of which SDK each call came from.\n\n### Not locked to any one provider\n\nDrop-in wrappers for OpenAI and Anthropic, first-class LangChain integration, and a direct REST API for anything else. Switching providers doesn't cost you your accumulated corrections — the memory layer outlives whichever model you're on.\n\n## Start free,\n\nupgrade when *you're ready.*\n\n*$0*\n\nThe free plan covers everything you need to integrate AgentLoop and see it working in your stack.\n\nNeed more headroom? Paid plans are live — usage-based and predictable, with no surprise bills. See current plans and limits inside the app.\n\n[View plans →](https://app.getagentloop.io)\n\n## Real human, *actually replies.*\n\nQuestions about whether AgentLoop fits your stack? Want to discuss production pricing? Write directly — we read every email.\n\n[hello@getagentloop.io →](mailto:hello@getagentloop.io?subject=AgentLoop)", "url": "https://wpnews.pro/news/learning-infrastructure-for-ai-agents", "canonical_source": "https://getagentloop.io/", "published_at": "2026-06-13 15:04:52+00:00", "updated_at": "2026-06-13 15:17:19.324138+00:00", "lang": "en", "topics": ["ai-agents", "ai-infrastructure", "developer-tools", "machine-learning", "large-language-models"], "entities": ["AgentLoop", "OpenAI", "Anthropic", "LangChain"], "alternates": {"html": "https://wpnews.pro/news/learning-infrastructure-for-ai-agents", "markdown": "https://wpnews.pro/news/learning-infrastructure-for-ai-agents.md", "text": "https://wpnews.pro/news/learning-infrastructure-for-ai-agents.txt", "jsonld": "https://wpnews.pro/news/learning-infrastructure-for-ai-agents.jsonld"}}