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.
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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Blue-green deployment is older than the cloud, and it still works. The twist for AI agents is that the unit of deploy is not "the binary"; it is the bundle of code, prompts, model version, retrieval index, and tool…
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Picking the wrong AI agent vendor costs more than the subscription fee. According to The Standish Group's CHAOS Report (2020), 66% of software projects end in partial or total failure, and poor vendor selection is a…
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I lost a customer because of 47 minutes of downtime. Not server downtime. The server was fine. The agent couldn't complete tasks because OpenAI's API was returning 503s, and I had no fallback configured. The agent…
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Most teams deploy AI agents and then track nothing. Or they track one metric, usually accuracy, and call it done. That approach misses most of the picture. According to Gartner's 2025 Agentic AI survey, only 29% of…
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An AI agent that calls five APIs holds five sets of credentials that an attacker can steal. That's not a hypothetical risk. The 2024 IBM Cost of a Data Breach Report found that stolen or compromised credentials…
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A single AI agent stuck in a retry loop can burn through thousands of dollars in API credits within minutes. According to a 2024 Stanford HAI report, enterprise AI projects regularly exceed budgets by 20 to 40…
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Your AI agent works. It answers questions, calls tools, returns useful output. But it takes eight seconds to respond, and your token bill keeps climbing. Sound familiar? Performance tuning is the difference between…
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Your AI agent just sent 4,000 customers the wrong refund amount. The clock is ticking. According to IBM's 2024 Cost of a Data Breach Report, organizations that contain breaches in under 200 days save an average of…
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Your company runs 15 agents across four departments. The LLM bill arrives as a single line item. Finance asks: "Who spent what?" You don't have an answer. That gap between aggregate spend and per-team accountability…
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Prompt injection is the #1 risk on the OWASP LLM Top 10 (OWASP, 2025). Agents amplify every LLM risk by adding tools, persistence, and autonomy. This checklist gives you 47 controls across 10 categories. Each control…
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