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#

1 week out: Omnigent & Multi-Agent Coding Meetup in NYC 🗽 Join us at the Databricks NYC office for talks on Omnigent, custom models, and multi-agent orchestration + governance, with speakers from @databricks, @modal, and @cursor_ai. 🙌 📅 Wed, Aug 19 🕒 5:00–8:00 PM ET 🎟️ RSVP: #Omnigent# #AIAgents# #CodingAgents#

2 weeks out ⏳ Omnigent & Multi-Agent Coding Meetup in NYC Join us at the @databricks NYC office with speakers from Databricks, @modal, and @cursor_ai. Expect a primer on Omnigent, a talk on custom models, a panel on multi-agent orchestration and governance, and mingling with bites and dessert. 🙌 📅 Wed, August 19 | 5:00–8:00 PM ET 🎟️ RSVP: #Omnigent# #AIAgents# #CodingAgents#

💰 Same model, same task, but swap the software wrapper (harness) around it and the cost can jump 5x while success rate barely moves. Title: HarnessTax: How Much Does the Harness Matter for Coding Agents? URL: UC Berkeley and Arena compared three coding agent harnesses, Claude Code, Codex CLI, and Pi, across 7 models and 21 model-harness pairs. Here are 3 highlights. 📊 Statistically, harness barely moves success rate Out of 42 within-model harness comparisons, only 1 was statistically significant (chance alone predicts about 2), and none survived correction for multiple comparisons. 💸 Cost can differ 5x at the same success rate On GPT-5.6 Luna, Claude Code cost $0.15/task at 55.6% success, while Pi cost $0.03/task at 53.3% success, nearly identical accuracy, 5x the price. 🪶 The minimal open-source harness holds its own In 9 of 12 model comparisons, a non-vendor harness (Pi or Codex CLI) achieved the top success rate, showing a stripped-down harness can genuinely compete. The practical takeaway: prioritize cost and reliability over flashy add-on features first. #CodingAgents# #LLMCostOptimization#

Turns out the best harness design for a coding agent — planning, tool setup, context management — can flip completely depending on how capable the model is. This paper tested 176 configurations to prove it. Title: An Empirical Study of Harness Design for Coding Agents URL: 🧠 Highlight 1: Context management matters most when resources are scarce At a 32k-token window, managed vs. unmanaged context created a 35.7-point gap in SWE-Bench success rate. Interestingly, the fancy "recall" mechanism was barely ever used and added no accuracy at all. 📋 Highlight 2: Planning's benefit flips with model strength A weaker 30B model gained +11.6 points from adding a planning tool, and its rate of quitting without even attempting an edit dropped from 69% to 28%. A stronger 550B model needed no such help — adding planning there just cut cost by about 30% with no accuracy change. 🔧 Highlight 3: Tool design also depends on the model and task Weaker models need a full dedicated toolset, while stronger models often perform better and cheaper with bash-only access. Even the same model can flip its optimal choice depending on the task type. This really drives home that tuning each harness component to your specific model and task is worth taking seriously. #CodingAgents# #LLM#

🧵 You told the agent on day one to never expose internal DB IDs. Sixty messages later it builds a new auth flow. Does it still remember? Title: Coding Agents Don't Need Longer History — They Need Intent Continuity URL: The article's premise: bigger context windows and RAG don't fix this. What's missing is a mechanism to check whether past intent still applies right now. Highlight ①🔍 A new axis: intent continuity RAG answers "what's relevant?" Longer context solves capacity. Neither answers "is this still true?" The framework splits recovery, validity checking, and application into distinct steps. Highlight ②⚙️ Supersession detection as the verification core When two records share the same target but disagree, the later one wins chronologically — but records in different scopes (prod vs. prototype) both stay active. This verification layer, the author argues, does heavier lifting than retrieval itself. Highlight ③📊 The numbers back it up On 70 synthetic interactions, keyword search hit 0.57 recall and passed only 4 of 8 tasks. The intent-aware approach hit 1.00 recall, zero violations, and passed all 8 — for about 28% more tokens. A quiet but essential lens for running coding agents on long-lived projects. #AIAgents# #CodingAgents#

🔬 Can AI actually "discover" science? Tested on 90 real Nature papers, even the strongest agent beat SOTA on under 20% of tasks. Title: NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers? URL: 📋 Overview Using 90 tasks drawn from Nature-family journals across six domains, this work tests whether AI coding agents can reproduce and surpass published SOTA. An "information firewall" hides the original method, and NatureGym builds per-task containerized environments for strict, unified evaluation with web search disabled. 🎯 The problem it solves Prior agent-on-research benchmarks were fragmented and low in credibility. This work forces genuine discovery instead of reproduction and creates a common ground for cross-domain comparison. 📐 Methodology Tasks are scored with a SOTA-normalized relative gap (g > 0.1 to surpass, g >= 0 to match), comparing across 81 metrics. Ten agents are evaluated across three harnesses under a 4-hour budget with task-based GPU allocation. 📊 Results ・Even the top agent, Claude Opus 4.7, surpassed SOTA on only 17.8% of tasks and matched on 47.8% ・45.5% of successes were just translating a science task into supervised prediction; genuine domain reasoning was only 8.3% ・Failures were driven by wrong method choice (45.1%) and insufficient compute (24.4%), with task misunderstanding at only 3.1% ・Interdisciplinary tasks consistently scored worse A sober snapshot: today's agents are good at "translation" but weak at real "discovery." #AI4Science# #CodingAgents#

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#

Coding agents can run shell commands, install packages, call MCP tools and touch your secrets. Maybe we should watch them. 😂 Prismor is an open-source firewall for AI agents. Every tool call gets checked BEFORE it runs. Allow. Warn. Approve. Block. Codex + Hermes included.

coding agents are moving fast from prototype to production. the infrastructure question is what's left. join @parthsareen from @ollama and @zainhas Hasan from Together AI at @AIconference for a breakout on what it actually takes to build coding agents on open models, and run them at scale.