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.
Venture money poured into AI agent startups again in Q2 2026, but the distribution tells the real story: a small number of foundation-model and coding-agent companies captured most of the headline capital, valuations…
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The defining AI agent security story of 2026 is not a single named company breach. It is the maturing of a handful of attack classes that researchers and standards bodies documented through 2024 and 2025, now showing…
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In 2026, AI agents can finally talk to tools and to each other through open standards rather than bespoke glue code. Two protocols dominate the conversation: MCP (Model Context Protocol) for connecting an agent to…
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AI agents fail in production in a small number of predictable ways, and the failure is almost never that the underlying model was too weak. It is that nobody built a guardrail, a test, a budget cap, or a human…
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Every agent has to answer two different questions. First, what should I do to reach this goal. Second, how do I actually do it. The first is planning. The second is execution. They look like one smooth motion from…
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An AI agent can only think about what fits in front of it. That "in front of it" is the context window: the span of text the model reads before deciding its next move. For a quick question it never fills up. For a…
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A useful AI agent is rarely one giant instruction. Underneath, the good ones are assembled from smaller parts: a tool that reads a calendar, a tool that sends an email, a packaged routine for drafting a reply, a…
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An AI agent is not one thing. Under the surface, the agent follows a control structure that decides how it reasons, when it calls tools, whether it checks its own work, and how it splits a job into smaller jobs. That…
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AI agent pricing in 2026 is genuinely confusing, because the platforms are not priced the same way. Comparing them on the headline number is like comparing a phone plan, a tank of gas, and a kit car by their sticker…
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The EU AI Act is the first comprehensive AI law from a major jurisdiction, and in 2026 its obligations are no longer theoretical, they are phasing in on a fixed schedule. For anyone building or deploying AI agents…
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I have pitched ideas that were technically better than the thing that got approved, and lost. The lesson stuck: the quality of an agent project rarely decides whether it gets funded. The quality of the buy-in does. A…
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Most teams do not migrate to AI agents because a vendor sold them on it. They migrate because a Zap broke on an edge case for the third time, or because a Make scenario grew into a 22-step chain that nobody dares…
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"How long will this take?" is the first question every buyer asks and the one most vendors answer badly. The honest answer is that it depends on which of four very different things you are actually building. A…
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An executive does not read a business case to learn. They read it to decide whether to bet a slice of budget and reputation on you being right. Everything in the document either reduces their uncertainty or wastes…
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Agent updates fail badly when they are in-place. A prompt edit lands mid-run and the second half of a conversation no longer matches the first. A new model lands and a tool-call signature shifts. An index rebuild…
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The vendor quote is rarely the cost. A platform that lists at $5K/month often has a real TCO of $12K to $20K/month once integration build, model usage, maintenance, governance, and change management are added. This…
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ROI calculations for AI agents fall into two categories: defensible numbers backed by measurement, and made-up numbers backed by vendor claims. The CFO can tell the difference. This guide is the defensible version:…
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An agent PoC succeeds when the go-no-go decision is obvious within 6 weeks. It fails when scope creeps, baseline is missing, or no one is responsible for the call. The 25-item checklist below covers what to confirm…
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This is the RFP template I send when buyers ask "give me the question list". Sixty questions across six sections, with a 1-to-5 scoring rubric and walk-away criteria. The template assumes enterprise procurement;…
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A PoC tells you the technology works. A pilot tells you the deployment works. Most teams skip the pilot because the PoC succeeded; then production hits real volume, real users, and real operational concerns, and the…
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The on-call runbook is the operational artifact that makes the platform survivable. A well-written runbook turns a 2 AM page into a 10-minute fix the on-call engineer can execute alone. A bad runbook forces a…
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Most agent platform outages I have seen were not catastrophic. A model provider had an incident; a region's vector store throttled; a deploy clobbered a prompt store; a tenant's run history was deleted by a buggy…
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Capacity planning for agent platforms looks like web-app capacity planning but with two big differences. The throughput unit is tokens-per-minute, not requests-per-second. The cost curve is steeper because each…
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SOC 2 is the buyer-facing artifact most enterprise prospects ask for before they let an AI agent platform near their data. It is also one of the most misunderstood. The report does not certify your AI; it attests…
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Most AI agent purchases go wrong at the sales-call stage, not after deployment. The team likes the demo, the vendor likes the deal, and a year later someone is paying for an unused seat tier with a 60-day notice…
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An AI agent dashboard does two jobs. It tells the on-call within a minute whether the platform is healthy. It tells a debugger within five minutes why a specific run went wrong. Most dashboards are good at one or the…
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The classic SaaS isolation problem is well understood: keep tenant data, queries, and identity separated through the request path. An agent platform adds two new surfaces that have to follow the same rules. The…
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Agent logs grow fast. A single run easily writes dozens of structured events: orchestrator steps, model calls with input and output bodies, tool calls with payloads, retrieval queries with chunk text. Multiply by…
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Most AI agent platforms first discover their real ceiling during a launch. The dashboard says everything is fine, the model provider's rate limiter starts throwing 429s, the retry loop multiplies the rate of incoming…
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The point of a canary is to learn things evals cannot. Evals run on a held-out set; production runs on whatever showed up today. Some regressions are visible only at production scale, on production traffic shapes,…
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