🚨 Agent security alerts often drown in noise. 🧭 AgentLoop Audit separates real risks from noise: ꔷ Full session & tool facts ꔷ Contextual risk analysis ꔷ One-click evidence location ꔷ Impact traced by secret, app & user 🔗  #AIAgent# #Security# #AgentSecurity# #AISecurity# #LLMOps#

🎮 Game agents can demo well but fail in production. 🔍 A 3-day, 7-step AgentLoop method: • Trace evidence, not claims • Evaluate tools & API facts • Fix root causes with Skill guardrails • Regress with datasets & alerts #AIAgent# #GameDev# #AgentLoop# #LLMOps# #CodingAgent#

A debugging session or a multi-agent run can rack up a big LLM bill before anyone notices. This blog by @TerryTangYuan covers how Omnigent handles that. Smart routing keeps trivial tasks off expensive models. Progressive budgets warn before they block expensive models. The Usage page shows spend by model, harness, and session. Read the full post 👉 #Omnigent# #AIAgents# #OpenSource# #LLMOps#

LLM bills spiral when debugging sessions or multi-agent workflows run away. Omnigent warns first, then downgrades, doesn't hard-stop mid-task. 👇 🛡️ trivial_gate routes trivial requests off Opus, Fable, GPT-5 💰 Progressive budgets block expensive tiers; work continues on Sonnet/Haiku 📊 /usage breaks spend by harness, model, and session Phased rollout: visibility → routing → budgets Read more: #Omnigent# #LLMOps# #GenAI#

Join us in Bellevue for the Agentic + AI Observability Meetup! 🙌 Focusing on agentic architectures, AI observability, and real-world deployment of Omnigent. 🔹 Talk #1#: Harness the Power of Omnigent - Zeyi Fan (@databricks) 🔹 Talk #2#: Governing and Improving Production Agents with Omnigent and Codex - Wesley Pasfield (@OpenAI) 🔹 Mingling with bites and dessert 🗓️ Thurs, Aug 27 | 5:00–8:00 PM PT 📍 Databricks Bellevue Office 🔗 Register: #Omnigent# #AgenticAI# #Observability# #OpenSource# #LLMOps#

Selecting the right model for a task, and for every subagent it spawns, is a decision you shouldn't have to make by hand on every run. Smart Routing makes it automatic. Turn it on once, and Omnigent routes each task and its subagents to the best model and harness for the job: 🔹 Open Source: an LLM classifier picks the best model 🔹 @databricks: powered by AI Gateway smart routing Learn more 👉 #Omnigent# #OpenSource# #AIAgents# #LLMOps# #CodingAgents#

Omnigent policies govern coding-agent pause points and return ALLOW, ASK, or DENY. They're contextual: 🔹 Session state + prior events (PII, budgets) 🔹 Built-in or custom (Python / CEL) 🔹 Add to a live session and see it fire Dive into our latest blog to learn more 👉 #Omnigent# #AIAgents# #LLMOps#

TL;DR Reliable agentic AI comes not from "a better model or prompt" but from explicitly engineering context and the orchestration harness—lessons from PRINCE, a Bayer × Thoughtworks pharma system. 🧪 Title: Building Reliable Agentic AI Systems URL: Highlights 🧭 Sequential agents with pause points: clarify intent → Think & Plan → Researcher → Reflection → Writer, verifying step by step 🔁 Three reflection loops: process (trajectory), data (evidence sufficiency), draft (output completeness) catch distinct failures 🔎 Hybrid retrieval: query expansion n=5, weighting 0.7 semantic + 0.3 keyword, bge-reranker cutting ~20 → 7 chunks 🗃️ Structured data via Text-to-SQL: SELECT-only, up to 3 self-corrections, ≤50 rows per query 🛟 Harness engineering: state persisted in PostgreSQL/DynamoDB, resume from failure point, automatic provider fallback 📌 Sentence-level citations plus RAGAS and Langfuse evals; daily batch checks on live traffic catch hallucinations 🏷️ NER extracts entities from study PDFs; high-confidence fields auto-update, low-confidence quarantined for human review The "even with big context windows, selectivity still matters" stance rings true to anyone shipping this stuff. #AIAgents# #LLMOps#

Coding agents fire dozens of API calls per task, so a single developer can quietly burn thousands of dollars a week before anyone notices — here's how LangChain killed that "spend unpredictability" internally 💸 The key was folding budget control into the same place as observability. Title: How LangChain Made Coding Agent Spend Predictable URL: 💸 Overview An LLM Gateway built into LangSmith gives a company-wide, minute-level view of model spend and manages budgets centrally. Rather than a bolt-on proxy, it sits on the same foundation as existing tracing, evaluation, and user management. ❓ Challenges Solved Model usage spread from a few teams to the whole company, and premium model prices rose, so costs spiked. ・Coding agents trigger dozens of API calls per task ・Individual developers ran up thousands of dollars a week, unnoticed until month-end 💡 Methodology & Proposed Approach Budgets can be set across multiple layers. ・Caps at the organization, workspace, user, and API-key level ・Default monthly, weekly, daily, and hourly windows for all employees, with exceptions for heavy projects ・Covers agents accessed via Claude Code, Codex, and LangChain Deep Agents ・Deployed via MDM so no one has to set it up manually ・Runs are traced and tied to a user and API key; overspend can be diagnosed by inspecting the trace with evaluation data 🌍 Use Cases Engineering leaders can set team-level limits while still letting people use agents without fear of a surprise bill. The practical value is replacing the month-end billing shock with real-time monitoring. 📊 Lessons & Outcomes ・Static price tables go stale fast, so pricing must be handled dynamically, including caching and tier differences ・Cursor and Claude Desktop didn't route cleanly, so they measured the delta between Gateway-captured traffic and provider settings to correct for it ・Hard limits alone block real work, so they evolved into early-warning alerts and auditable budget-increase requests ・Since internal rollout, LLM costs have stayed within budget #CodingAgents# #LLMOps#

Logging alone won't prevent incidents. Meet runtime governance that blocks problematic requests before they reach the LLM 🛡️ Title: LangSmith LLM Gateway: runtime governance built into the agent lifecycle URL: 🛡️ Overview A runtime governance layer that sits between agents and their model providers. As an enforcement point inside the LangSmith platform, it aims to stop problems at the source rather than just logging them after the fact. ❓ Challenges Solved Observability (logging) alone can't prevent problems. Logging an incident after it happens is too late — problematic requests should be blocked before they reach external LLM providers. 💡 How It Works ・Spend controls: hard caps at org, workspace, user, or API-key level; returns 402 when exceeded ・Cost visibility: real-time spend tracking across org units ・Data protection: auto-redaction of PII and secrets before they reach the model ・Trace integration: gateway-proxied calls appear in the same workspace ・Audit logging and layered policies Setup is minimal: point base_url at the Gateway, store provider keys in workspace secrets, and define policies in the LangSmith UI. 🌍 Use Cases ・Preventing runaway agent spend from retry loops ・Stopping sensitive data (SSNs, PII) from leaking into provider logs ・Establishing org-wide cost governance and compliance auditing #LLMOps# #AIGovernance#