Windsurf combines collaborative chat behavior with agent autonomy, tool calling, and codebase context in one interface.
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Cascade tracks a developer's edits, commands, recent files, and intent to make its suggestions fit the current task.
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Engineers get better results by exploring, planning, building, testing, and committing with Cascade instead of leaving it to work alone.
Summary
Eashan Sinha presents Windsurf's Cascade as a peer programmer for experienced engineers. He describes the shift from autocomplete and chat toward agents that retrieve context, call tools, and work through larger tasks iteratively. Windsurf combines those agent abilities with a collaborative interface and tracks the developer's actions, edited files, terminal commands, clipboard, and inferred intent. Its context engine uses several approaches to understand the codebase, while workflows, rules, memories, MCP servers, and simultaneous Cascade sessions give developers more control. Sinha recommends an engineering process of discovery, planning, implementation, testing, and review. Developers should create a planning file, define the desired outcome, give direct file and directory context, answer Cascade's questions, and correct it during the work. The talk is honest that agents still need supervision. The useful pattern is close collaboration, followed by tests and a Git commit only after the definition of done has been checked.
Coding agents became useful when they could act across multiple steps
Sinha contrasts three stages of AI-assisted development. In 2022, engineers wrote code themselves and relied on Stack Overflow and Google. In 2022 and 2023, copilots such as ChatGPT, Bard, and GitHub Copilot handled chat responses and autocomplete, but the developer had to provide context and interact at each step. By late 2024, agents could retrieve context, call tools, work independently, adjust their trajectory, and handle larger tasks. Windsurf combines the collaborative nature of a chatbot with the autonomy and tool use of an agent.
Cascade uses the developer's activity to infer intent
Windsurf's idea of flow awareness includes more than the current prompt. Cascade tracks edits, commands, terminal commands, clipboard contents, and recently edited files. It puts that activity into the agent's trajectory so the next steps fit what the developer is doing and may do next. Sinha compares this to predicting the next token, except Cascade is inferring the user's next action. The goal is an interaction that feels like the tool understands the developer over time rather than behaving like an independent, general-purpose agent.
Sinha says Windsurf does not rely only on retrieval augmented generation or embedding-based search. Its context engine combines multiple tools to understand the codebase and provide explicit context. Cascade can then ground its suggestions in the project's source of truth instead of guessing from limited information. Sinha connects this to more accurate and relevant results, fewer hallucinations, and recommendations tailored to the codebase. The same context supports Windsurf Tab and Cascade, so autocomplete and agent actions can reflect the work already happening in the project.
Workflows and rules give an autonomous agent more predictable behavior
Cascade can call MCP servers and use workflows. Sinha describes workflows as a way to narrow the gap between an agent's unpredictable actions and a workflow's predetermined sequence. Developers can define steps for Cascade to follow while it operates. They can also create file-based rules or rules that always or sometimes apply. Cascade can retain memories about developer preferences and the codebase, reducing the need to repeatedly index and retrieve the same information. Multiple simultaneous Cascade sessions can follow different trajectories while sharing an understanding of the user and codebase.
A planning document keeps the developer and agent aligned
Sinha recommends a process that starts with exploring the codebase and scoping the task, then moves through planning, building, and testing. During planning, the developer should define the desired outcome and create a file with the tasks, often using checkboxes. Rules can describe how Cascade should behave. During implementation, Cascade should work from that plan, edit one or more files, and check off the work. The developer should answer its questions clearly and correct its direction when needed. Mentioning specific files and directories supplies more direct context.
Testing and feedback are part of the agent interaction
After implementation, Sinha advises developers to generate tests with Cascade, run them, and fix failures one at a time as the agent iterates. Workflows can automate parts of this process. The developer should then check whether Cascade met the definition of done. If it did not, the developer should explain what went wrong. Sinha says Cascade builds a representation of the developer from this feedback, allowing it to learn where it made mistakes and improve its future behavior. A Git commit comes only after the code is functional and the work has been checked.
Cascade needs close supervision rather than unattended delegation
Sinha rejects the idea that a developer should give Cascade a task and return thirty minutes later to inspect the result. He says current language models are not yet at that level. The developer and Cascade should work as a team, with the developer checking in throughout the task, directing the order of work, and pointing out incorrect decisions. His advice is to treat Cascade as a peer programmer and make the interaction collaborative. The quality of the result depends on the developer staying involved in the process.
"Cascade tracks their actions, their edits, their commands, the terminal commands they've run, anything that's in their clipboard, all the files that they've recently edited."07:47
Who should watch
You are a senior or staff engineer deciding how to use an agent on a large, unfamiliar codebase.
Your current AI workflow is mostly autocomplete or chat, and you want a process for larger multi-step tasks.
You want practical guidance on planning, rules, testing, and supervision before letting an agent change production code.
