Q
Can autocomplete tools become agentic?
Many are adding agent modes. The contract is what changes: once a tool edits files and runs commands, it inherits the guardrail requirements (approvals, sandbox, verification) whether or not it ships them.
Blog · 2026-09-15 · 6 min
Agentic Coding vs Autocomplete: Different Tools, Different Contracts.
One predicts your next edit; the other owns a task to completion. The right choice depends on what failure costs.
Direct answer
Autocomplete and an agentic harness are not versions of each other. They optimize opposite things: keystroke-level speed versus task-level completion, with a very different tolerance for being wrong.
01
The contracts
Autocomplete answers 'what comes next in this file?' in milliseconds, statelessly, with an error that is cheap to ignore. An agentic tool answers 'get this task done' across files, with real tools, over many rounds, and an error that propagates into commits, so it needs guardrails the autocomplete case never requires: approvals, sandbox policy, round limits, context budgets.
That difference shows up in how you measure them. Autocomplete is judged on acceptance rate and latency. An agent is judged on completed-and-verified work per unit of cost, which is why our own benchmark program treats an external verifier, not a model's self-report, as the definition of done.
02
Where each one wins
Autocomplete wins in the inner loop: writing a function, filling a test, mechanical edits you already understand. It stays out of the way and costs almost nothing to be wrong. Agents win at the outer loop: tracing a bug across modules, applying a codemod, updating every call site, running the tests, and reporting what happened, work that is mostly reading, searching and verifying, where a human's attention is the bottleneck.
The productive setups stack both: autocomplete for typing, an agent for the multi-file work, and a memory layer under the agent so the tenth session on the repository starts from what the first nine learned.
03
What guardrails buy you
An agent without boundaries is a liability, not a feature. Read-only plan mode lets it explore without mutating. A declarative sandbox policy limits reads, writes, exec and network regardless of approval mode. Output caps keep a single command from poisoning the context. And an integration gate keeps autonomous work on a reviewable branch instead of your working tree.
None of this makes the agent smarter. It makes the failure modes small enough to run unattended, which is the actual precondition for delegating work instead of just getting suggestions.
04
Choose by cost of being wrong
If a wrong suggestion costs you two seconds, take the fast tool. If a wrong change costs a CI cycle, a review round, or a rollback, the overhead of approvals and verification pays for itself immediately. Most developers do not need to pick one philosophy; they need to know which loop they are in before they start.
Questions
Asked About This Post.
Q
Is agentic coding only for large tasks?
No, but it earns its overhead on tasks with multiple steps or files. For a one-line edit, the inner-loop tool is simply faster.
Q
Does an agent replace code review?
No. External verification catches what the verifier covers; review catches intent. Agentic work still merges through the same gates as human work.
Run Agents That Fit On Your Laptop.
25.6 MB median RSS. 25 agents ran in parallel on a Core 2 Duo with 4 GB RAM. Hundreds on your machine. Zero cloud required on the Ollama path.
Requires Rust/cargo to build from source. Linux and macOS today, Windows not yet supported. Pre-1.0, public beta. Pricing TBD.