"The cyber attack seems like a very possible scenario because it's a way for the banks to absolve themselves of any role in a financial crisis." Pretty scary and more than likely when you think about it.

Exactly. Model upgrades can improve fluency and speed while leaving the operating risk unchanged. The harness determines what the agent can know, do and commit—and whether the business can audit, interrupt and recover when the answer is wrong.

A shared machine turns bot handoffs into a design problem. "Instead of a machine per task, you get a machine per person." Every bot keeps its own role, memory, and thread while sharing one persistent computer, browser state, files, and connections. Name it the Routine Readiness Gate. A task becomes a routine only after the bot can repeat the work reliably on the shared machine. 1) Run one real task before saving any skill. 2) Correct the bot until its method produces dependable work. 3) Save durable files in the shared workspace. 4) Prefer a connector, then use the browser when no connector exists. 5) Use takeover only for the blocked authentication step. 6) Schedule the routine only when another bot can share the computer without breaking the run. A bot can own up to 50 routines, so the scarce resource is not routine capacity. It is confidence that shared state, browser changes, and handoffs will not corrupt repeated work. On Monday, keep a task manual unless it passes all six checks.

Model choice cannot rescue a weak production system. "The model is interchangeable. The harness is not." LangChain's coding agent improved from 52.8% to 66.5% on Terminal Bench 2.0 without changing model parameters. The system around the model changed. A production harness controls context, execution, memory, and the boundaries between agents. Before an agent touches production: 1. Keep the root instruction file small. Load task-specific context only when needed. 2. Give each agent a narrow role and only the tools that role requires. 3. Store decisions, failures, and session state outside the model's context window. 4. Require separate steps for research, planning, execution, and verification. 5. Enforce spending limits, approved tool lists, test gates, rollback paths, and a complete action log. 6. Do not ship unless you can stop and reverse a bad action, then reconstruct it from the audit trail. Without this harness, each session forgets context and compounds the same failures.

the important part of Hermes v0.21 is not that agents can talk to each other chat is the interface the architecture underneath is what turns separate bots into a durable team: identity > each bot has its own role, model, memory, skills, and credentials addressability > agents can message a specific peer across profiles and gateways continuity > handoffs land in canonical chats and scheduled jobs remember previous runs intervention > running subagents can be inspected, redirected, or stopped contracts > delegated work can require schema-validated output accounting > each delegation reports its own cost governance instruction files require approval before an agent can rewrite them without these primitives, “multi-agent” is several prompts coordinating through shared amnesia with them, the unit of work stops being a chat it becomes an inspectable organization that can remember, delegate, correct course, and continue tomorrow the Pantheon release below ↓

when you have been in survival mode, a five-year plan can become avoidance dressed as ambition your decision horizon has collapsed do not begin with reinvention begin by restoring participation the 30-day Return Protocol: STOP THE LEAK end one active source of damage CONTACT REALITY write down the facts, obligations, and available capacity REPAIR THE SYSTEM stabilize sleep, food, movement, and environment RETURN THROUGH WORK complete one action that leaves evidence every day REDUCE RELAPSE FRICTION prepare tomorrow before today ends the goal is not to repair your entire life in one month it is to stop disappearing from it you cannot recover the years you lost you can stop giving them more years Save the full Return Protocol below ↓

A reference to Engineering Reliable Coding Agents in the wild we need better observability into the failure mechanisms of agentic systems, it's kind of silly to see leaderboards that rank by model like it's everything

10 AI Trading GitHub Repos You Should Have on Your Radar: TradingAgents Multi-agent trading firm with analysts, researchers, risk managers, and a portfolio manager. AI Hedge Fund Investor-inspired agents debating valuation, sentiment, fundamentals, and risk. FinRL-X AI-native infrastructure for data, strategy development, backtesting, risk and execution. Qlib Microsoft’s platform for ML-based quantitative research and production workflows. RD-Agent Multi-agent factor discovery and model optimization for quantitative research. FinGPT Open-source financial language models, datasets and sentiment pipelines. FinRobot LLM agent platform for financial analysis, research reports and strategy workflows. AI-Trader An agent-native platform where AI agents can generate and exchange trading signals. Agentic Trading Lab Traceable experiments, backtests, paper trading, and decision logs for LLM agents. TradingGoose Multi-agent research and portfolio analysis built around market-moving events. The opportunity is not asking an LLM whether to buy a stock. It is building a measurable loop: >data >hypothesis >signal >sizing >execution >evaluation >memory Treat these as research infrastructure, not plug-and-profit bots. Save the list. Fork the systems. Verify every result.

when you have been in survival mode, a five-year plan can become avoidance dressed as ambition your decision horizon has collapsed do not begin with reinvention begin by restoring participation the 30-day Return Protocol: STOP THE LEAK end one active source of damage CONTACT REALITY write down the facts, obligations, and available capacity REPAIR THE SYSTEM stabilize sleep, food, movement, and environment RETURN THROUGH WORK complete one action that leaves evidence every day REDUCE RELAPSE FRICTION prepare tomorrow before today ends the goal is not to repair your entire life in one month it is to stop disappearing from it you cannot recover the years you lost you can stop giving them more years Save the full Return Protocol below ↓

10 AI Trading GitHub Repos You Should Have on Your Radar: TradingAgents Multi-agent trading firm with analysts, researchers, risk managers, and a portfolio manager. AI Hedge Fund Investor-inspired agents debating valuation, sentiment, fundamentals, and risk. FinRL-X AI-native infrastructure for data, strategy development, backtesting, risk and execution. Qlib Microsoft’s platform for ML-based quantitative research and production workflows. RD-Agent Multi-agent factor discovery and model optimization for quantitative research. FinGPT Open-source financial language models, datasets and sentiment pipelines. FinRobot LLM agent platform for financial analysis, research reports and strategy workflows. AI-Trader An agent-native platform where AI agents can generate and exchange trading signals. Agentic Trading Lab Traceable experiments, backtests, paper trading, and decision logs for LLM agents. TradingGoose Multi-agent research and portfolio analysis built around market-moving events. The opportunity is not asking an LLM whether to buy a stock. It is building a measurable loop: >data >hypothesis >signal >sizing >execution >evaluation >memory Treat these as research infrastructure, not plug-and-profit bots. Save the list. Fork the systems. Verify every result.

the important part of Hermes v0.21 is not that agents can talk to each other chat is the interface the architecture underneath is what turns separate bots into a durable team: identity > each bot has its own role, model, memory, skills, and credentials addressability > agents can message a specific peer across profiles and gateways continuity > handoffs land in canonical chats and scheduled jobs remember previous runs intervention > running subagents can be inspected, redirected, or stopped contracts > delegated work can require schema-validated output accounting > each delegation reports its own cost governance instruction files require approval before an agent can rewrite them without these primitives, “multi-agent” is several prompts coordinating through shared amnesia with them, the unit of work stops being a chat it becomes an inspectable organization that can remember, delegate, correct course, and continue tomorrow the Pantheon release below ↓