The task an agent picks up starts somewhere messy: a message in a channel, a line in a planning doc, a bug buried in a customer thread. Most of the time it also builds on work the team already did. Turning that raw signal into a task an agent can act on, with the right context carried forward, is where most of the output quality is decided. This is context engineering: the work of shaping intent and context into something an agent can build against.
For years it didn’t need designing. An engineer picked up a vague ticket and filled the gaps from experience: they knew the system, knew who to ask, and knew which unwritten constraints applied. The ambiguity got resolved quietly on the way to writing the code. An agent has none of that. It builds exactly what the task describes, and it fills gaps with guesses rather than judgment. Without the right context, code gets generated faster and productivity still takes a hit, because the wrong thing got built quickly. Ambiguous in, expensive out
When a poorly formed task reaches an agent, the cost doesn’t show up right away. The agent produces something plausible, the work moves forward, and the mismatch between what was meant and what was built surfaces later, in review or after it ships. By then it’s more expensive to unwind than it would have been to specify correctly at the start. Across a team running many agents, vague work compounds faster than any reviewer can catch
Context is a design problem, not a discipline problem
If the fix is just to write better tickets, that treats a structural gap as a personal failing, and it doesn’t holdup when work originates in a dozen places at once. The organizations getting ahead treat context as something to build, so that turning a raw signal into agent-ready work is a repeatable step rather than a task on individuals’ plates. In practice that means you can: Give raw signal one place to land. When an idea can come from a chat message, a document, or a support conversation and be captured as a task in the same system where it will be tracked and acted on, the intent survives the trip.
Make the task carry its own context. A well-formed task states what outcome it wants, what constraints apply, and what “done” looks like. On top of that, the strongest setups also let an agent draw on the surrounding body of work, the related decisions and prior tasks, so it inherits the situational awareness an engineer would have brought in without being told.
Close the gap while the intent is fresh. The moment to resolve ambiguity is when the task is created, while the person who understands it is still in the loop, not after an agent has acted on the unclear version.
Every finished task is the next task’s starting point
This isn’t a factory line where every task is the same. Each finished piece of work feeds more context into the next, and the leader’s job is to build the machine that captures it. Work comes in, an agent acts on it, and the result becomes part of the ground the next task stands on.
When an agent finishes, the summary of what it did belongs back in the same system the work was tracked in, attached to the thing it acted on, available to the next person or agent who touches that area. Do that consistently and the context compounds: each finished task leaves the ground clearer than it found it, so the next task arrives with more to build on and needs less repair.
What this means for leaders
Investing in agentic engineering means investing in context: clear, context-rich tasks produce output that reflects what the team intended, while vague ones produce motion that has to be corrected. The job isn’t to write better tasks one at a time. It’s to build the system that turns every finished task into context for the next one, so the work gets easier as it compounds.
See how leading engineering organizations turn raw signal into agent-ready work at jira.dev.