Agents act through integrations. iPaaS and API vendors hold the connectors, credentials and audit trails, and MCP makes governing that layer worth more.
Most tasks run better on one well-tooled agent. Multi-agent systems pay off only for parallel work, isolated context or independent checks.
The first agents trusted with company money will buy for the enterprise. Control moves from approval chains to policy as code and card-level limits.
Agent approvals catch errors only at irreversible actions, with a clear diff, at a pace reviewers can read. Where to put them, and how to tell they work.
Which IT operations actions AI agents can safely take, at what autonomy level, with which controls, and how to measure it without fooling yourself.
Most systems called agents should be workflows with a model call inside. The four shapes, a scoring rubric, and when autonomy is worth what it costs.
When an agent moves money or promises a customer something, the loss lands wherever the contract is silent. That gap is a deployment constraint.
When an agent fails in production the cause is usually a badly designed tool, not a weak model. The granularity, description, parameter and return rules.
The best vertical AI companies stopped selling assistants beside the suite. They are taking over the record itself, and the record owns the renewal.
Agent memory is a retention policy, not a database. The four memory classes, write and read rules, forgetting as a feature, and the store-or-recompute math.
Container, microVM, or separate account: what each agent sandbox rung actually stops, why egress policy matters most, and how to scope agent credentials.
Classic LLM observability breaks for agents. The telemetry model, four failure classes, trajectory evaluation, and a build order a platform team can ship.
Deep analysis across the systems, strategies, and economics that shape modern technology.
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