Plandex AI: how to review pending changes when a multi-file agent builds the whole plan first

2026-04-28 · 5 min read · ZenCode

Plandex is an open-source, terminal-based AI coding engine designed for complex, multi-file tasks. Unlike inline autocomplete tools, Plandex works by first generating a multi-step natural-language plan — “Step 1: create the auth middleware; Step 2: update route handlers; Step 3: add test coverage” — before writing a single line of code. Once the plan is approved, Plandex executes each step and accumulates all proposed changes in a “build” buffer. You review the pending diff, then apply it selectively or all at once. The workflow is designed for tasks that would require many separate prompts in a standard chat-based AI tool.

This design produces review traps that are different from the traps in other agentic tools. The traps are not about watching an agent work (as in OpenHands) or approving tool use in real-time (as in Cline). They arise from the plan-first architecture: a written plan you approved sits between you and the code that implements it, and reviewing the diff through the lens of “does this match what I agreed to?” is a different and weaker question than “is this code correct?”

The three traps

1. Plan approval as code pre-approval

Plandex presents a natural-language plan before making any changes. The plan names the files it will touch, the steps it will take, and the logic it will apply. Reading “Step 2: Update server/auth.ts to extract the JWT from the Authorization header and attach the decoded user object to the request context” gives you a clear picture of what will happen. Approving it feels like a technical decision you made with full information.

The trap is that approving a plan is not the same as reviewing code. The plan describes intended semantics. The code that implements those semantics can be correct at the semantic level and still fail at the implementation level: wrong function signatures, missing null checks, incorrect token validation logic, incomplete error handling for expired or malformed JWTs. The plan cannot surface these failures because the plan is written in the same register as the problem statement, not in the register of implementation details where errors live.

When the build completes and the diff appears, the natural review question becomes “did Plandex implement what it said it would?” — an instruction-compliance check rather than a correctness check. Instruction compliance is easier to satisfy than correctness. Code that does exactly what the plan described can still be wrong if the plan didn’t account for the case where the JWT has expired, or where the user object is missing a field the downstream route expects. The plan approval creates a reference frame that substitutes compliance verification for quality evaluation.

The fix: after a Plandex build completes, run plandex diff and read the raw code changes before re-reading the plan. The plan is intent; the diff is fact. When you read the diff without the plan as a frame, you evaluate whether the code is correct on its own terms — whether the logic handles all cases, whether the error paths are complete, whether the types are right — rather than whether it matches the description you approved. Reversing the review order costs nothing and breaks the plan-tinted evaluation reflex.

2. Session accumulation trust

Plandex is designed for multi-turn sessions that build complex features across many files. A non-trivial Plandex session might run 8–15 steps: load context, plan, execute steps 1–3, review and approve, continue with steps 4–7, review and approve again, and so on. Each step that goes well — types correct, logic clear, tests passing — raises the trust prior for the next step. By step 10 of a 12-step plan, the accumulated track record of correct earlier steps creates real cognitive pressure to approve later steps with less scrutiny.

This is the same dynamic as Aider’s re-prompt tax, but steeper. With Aider, rejecting a diff means re-prompting; with Plandex, abandoning a multi-step build at step 10 means discarding 10 steps of accumulated work and potentially restarting the entire session. The sunk-cost asymmetry is larger, and it peaks precisely at the steps where accumulated trust is highest — which are also the steps where the cumulative risk of prior errors compounding is highest.

There is a secondary dimension: session-level context drift. Plandex maintains a conversation context that includes your original instructions, prior steps, and all generated code. In long sessions, constraints you specified in the first message — “no new external dependencies,” “all errors should return, not throw,” “keep backward compatibility” — occupy early context positions that are progressively compressed as the session grows. The code generated in step 11 may silently violate a constraint from step 1 because that constraint is no longer prominent in the active context, even though you specified it explicitly.

The fix: before each new Plandex prompt in a multi-turn session, prepend the core constraint as a one-liner comment. “(constraint: no new npm dependencies, Python 3.11 only, all exports must stay backward-compatible) — now continue with step 8.” This takes five seconds and forces Plandex to surface the constraint in the current context position, not just in compressed earlier history. It also gives you a reference to check the output against before approving each sub-build: read the constraint, then read the diff through the lens of whether this specific constraint was respected.

3. Pending-changes buffer breadth

Plandex accumulates all proposed changes in a buffer before you apply anything. A complex task might produce a pending build that spans 15–25 files. When you run plandex changes to review the buffer, the diff is presented file by file in the order Plandex built them. That order typically follows the dependency graph: foundational types and schemas first, then the modules that depend on them, then the logic modules, then the tests.

The review trap follows directly from this order. The foundational files — type definitions, interface declarations, schema updates — are almost always correct, because they are structurally simple and exactly constrained by the problem statement. Reading three correct files builds trust. By the time you reach the behavioral files — the logic that uses those types, the handlers that implement the business rules, the error paths — you have already processed a long sequence of correct changes. The prior for each subsequent file is higher than it should be, and the behavioral files are exactly where Plandex is most likely to have drifted from intent, introduced incomplete error handling, or made assumptions about upstream behavior that don’t hold in all cases.

The presentation of a large unified buffer also creates an “apply all” pull. The natural terminal flow is: review, approve, apply. When the buffer spans many files and the first fifteen look correct, the apply action is what the workflow expects next. Selective application requires an additional deliberate step, and the brain’s default is to complete the expected workflow pattern.

The fix: when reviewing a Plandex build with plandex changes, navigate to the last file in the list first. Scope creep — files Plandex added that you didn’t explicitly request — and speculative logic extensions cluster in late-build files. Reading them before the structural early files catches additions before accumulated trust from correct earlier changes makes them harder to reject. If the last file looks wrong, you can abort before investing attention in the rest of the build.

How this differs from similar tools

The review challenge in Aider is structurally similar — a terminal-based agent that produces a diff you approve in bulk — but the plan layer is absent. With Aider, you review the diff against your original prompt directly. With Plandex, the plan sits between the prompt and the diff, and the plan approval creates an intermediate reference frame that filters how you read the code. The plan-tinted evaluation trap in Plandex has no equivalent in Aider.

Devin has the same plan-approval trap: a natural-language task specification that gets approved before code is written, with the specification substituting for code review. Devin operates through a web interface and produces changes in a remote sandbox; Plandex is terminal-native and accumulates changes locally. The plan approval framing is similar, but the pending-buffer breadth trap is specific to Plandex’s design — Devin doesn’t present a large unified diff for selective application before execution.

OpenHands shares the session sunk-cost trap: after 10–20 turns of autonomous building, rejecting the output means abandoning the session. The difference is that OpenHands shows execution logs in real time while Plandex accumulates silently. Both reach the same review problem at the end — a large set of changes to evaluate under sunk-cost pressure — via different paths.