Plain-English explainers for AI agent concepts: tool use, memory, orchestration, evaluation, safety, refusal policy, stopping conditions, and the rest of the agent stack. Written for non-researchers who need to make build vs buy calls.
Monitoring an AI agent is monitoring a non-deterministic distributed system. The four classic golden signals (latency, traffic, errors, saturation) translate. You need three more: token cost, tool success, and…
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A handoff is the contract between two agents (or one agent and a human) that specifies what gets passed, when, and what happens if the receiver is unavailable. Eight patterns cover most production cases. LangGraph,…
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Naive retries amplify outages; smart retries absorb them. The Google SRE book defines a retry budget so retries can't exceed a fixed fraction of normal load (Google SRE, ch. 22). For AI agents the same logic applies…
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This is the operational companion to AI agent cost models explained. That post covers the pricing axes vendors charge along. This one covers the tactics that reduce the bill in production. Most teams discover the…
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OWASP added "excessive agency" to the LLM Top 10 as LLM07 in 2025 (OWASP, 2025). The remedy is not fewer agents. It is bounded ones. An agent without a blast-radius bound turns a single prompt injection into a…
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Builders keep asking the same question: is this agent actually worth shipping? Most answers floating around treat AI agents like SaaS products with seat counts and CACs. They are not. An agent is a piece of software…
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Most builders look at a marketplace headline split, 70/30, 80/20, 95/5, and stop reading. That's the expensive mistake. The percentage is the smallest variable in the equation. What matters is everything sitting…
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Most teams don't need another tool to build an AI agent. They need an agent that already works. According to a 2025 McKinsey State of AI survey, 78% of enterprises now use generative AI in at least one business…
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Most AI agent ROI math undercounts cost and overcounts value. The honest version puts everything on the table: token cost, tool calls, infra, observability, retry overhead, and human-in-loop time on the cost side;…
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AI agent pricing pages are designed to look comparable when the models behind them are not. A flat-fee platform at one hundred dollars per month can be cheaper or more expensive than a usage-based platform at five…
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"No vendor lock-in" is one of the most overused phrases on enterprise pricing pages. Every platform has some lock-in. The honest question is which lock-ins you can live with and which would be catastrophic if you had…
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The "most integrations" claim is the cheapest one a SaaS marketing page can make. Counting connectors does not tell you whether the platform can do real agent work inside each one. A platform with five hundred…
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Production AI agents need updates. Models improve. Prompts get tighter. Tools get added. The team finds a way to make the stopping rule clearer. The question is not "should we update" but "how do we update without…
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The single hardest non-model problem in agent engineering is state. The model is stateless. Every other part of the system that gives it the illusion of continuity, of memory, of resumption, is your code. Get it…
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Code without version control is a hobby. Prompts without version control are a liability. The reason most agent prompts produce silent regressions in week six is not that the prompt got worse; it is that nobody can…
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Prompt engineering for AI agents is not the same craft as prompt engineering for chatbots. A chatbot prompt shapes one response. An agent prompt shapes a loop: the model picks a tool, reads the result, decides…
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The intuition that "more agents will do better than one agent" is wrong more often than it is right. Most production multi-agent systems exist because the work has genuine boundaries (different access controls,…
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Most AI agent failures in production are not model failures. They are integration failures. A webhook arrives twice and the agent acts twice. An OAuth refresh fails silently and the agent runs unauthenticated. A…
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The model is not the safety system. The model is the part you are deploying. Every control that protects users, data, and the bill from the model goes around it, not inside it. This guide is the runtime controls…
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Time-to-first-action is the most honest deployment metric for AI agent platforms. Marketing pages quote "deploy in minutes" without saying what counts as deployed. This benchmark sets a fixed task, an inbox-triage…
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Data residency is one of the silent gating items for enterprise sales of AI agents. The product can be perfect, but if the prompts leave the EU, the deal dies. This guide is the architecture playbook for…
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An AI agent's audit trail is the difference between "the agent took an action" and "we know why the agent took that action." It is what enables incident response, compliance audits, and the kind of post-hoc analysis…
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Most security guides written for large language models stop at the prompt boundary. They assume a single completion, no tools, no state, no autonomy. That model has not described production deployments for at least…
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Most engineers transition from testing deterministic services to testing agents and discover their old reflexes do not work. Unit tests assume identical inputs produce identical outputs. Agents do not. Integration…
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The first time I shipped an agent without proper observability I did not notice quality degradation for nine days. Token costs were stable, latency was fine, error rates were nominal. The agent was answering…
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For most of 2024 and the first half of 2025, AI governance for agents was a tomorrow problem. By mid-2025 it had become a this-quarter problem. The EU AI Act began entering force in stages, the NIST Generative AI…
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An agent that handles errors well looks identical to one that does not, right up until the day a tool returns a 500 in the middle of a refund chain and the customer ends up with the money back but the access revoked.…
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This is not a primer on AI agent pricing models. The taxonomy of per-token, per-task, per-agent, and capability-based pricing already lives at AI agent cost models explained. This piece is the operational sibling.…
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A "watch list" agent is the simplest, most useful agent most people never bother to set up. It polls a small number of listings on your behalf, applies criteria you specify once, and alerts you when something…
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A weekly newsletter is the most resilient distribution channel a founder has. Algorithms change; inboxes do not. The cost is the time you spend assembling the issue. An AI agent can pull that cost down without making…
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