The guides, comparisons, and customer stories behind how teams do it, in one place.
How Intraprise TechKnowlogies built a hybrid work-management system on DevStride — visibility and predictable cadence across 20–30 concurrent client projects.
Read the case study ↗How Tenger Ways runs every client engagement on DevStride, delivering measurable business value.
Read the case study ↗How BEGiN Learning replaced five trackers and the spreadsheet holding them together with one system — a live view across every project, and $50K+ a year saved in licenses.
Read the case study ↗The argument about whether AI will replace project managers is the wrong argument. AI and people are good at almost opposite things. The teams winning right now are not choosing between them, they are combining them on purpose. Here is the division of labor that actually works in delivery.
Read ↗AI can now read your plan, reason over your delivery metrics, and propose real changes to the work. That is a genuine step-change in speed, and a genuine new way to lose the plot. Here is the operating model that keeps both the speed and the judgment, drawn from how the DevStride team runs its own delivery.
Read ↗Every team has lived it: the board is green, the standups are calm, and then a date slips and no one saw it coming. The board did not fail you by accident. It failed you by design, because it tracked motion instead of meaning. Here is why, and what a connected model does about it.
Read ↗When an agent can generate work faster, vanity metrics get easier to hit and harder to trust. Here is how to read throughput, velocity, and cycle time so the numbers reflect delivered value, not motion, including the one adjustment most tools skip: netting out reopened work.
Read ↗Most AI oversight is a sentence in the docs asking you to review before you accept. Under deadline pressure that sentence loses. Here is the alternative: a guardrail built into the workflow, where the agent proposes, a human approves, and the system records, so the oversight is something you get, not something you hope for.
Read ↗"Human in the loop" has become a slogan people say and do not build. A loop where the person rubber-stamps whatever the agent produced is theater with an extra step. Real oversight means the human is making a decision, not confirming one. Here is how to draw that line honestly.
Read ↗Handing planning to an agent usually goes one of two ways: it produces a wall of tasks nobody trusts, or it quietly drifts from what you actually meant. The plan-and-build loop is the method that avoids both. Here is how it runs, drawn from how the DevStride team plans and ships its own work.
Read ↗A single AI review is a confident opinion, and confident opinions miss things. The teams getting real quality from AI do not trust one pass. They make the work survive scrutiny from more than one reviewer before a human signs off. Here is how to build that into how you ship.
Read ↗The single most important guardrail for AI-built work is boring: it lands in staging, and a person signs off before it reaches production. Not a modal, not a policy, a state the work has to pass through. Here is why that one gate does more for trust than any amount of model tuning.
Read ↗Most project-tool MCP servers let an agent look up a ticket. DevStride's lets an agent read your live plan, reason over real delivery metrics, and propose real changes, with a human keeping the final say. Here is the full connect-and-run walkthrough.
Read ↗Occasional, useful writing on delivery, dependencies, and doing more with AI. No noise.