AI Coding Agent
Reliability
Measure where coding agents drift, churn, freeze, skip tests, over-edit, or claim progress without evidence — before you scale them across engineering workflows.
Built from Iboga, a checkpoint-steering research harness for long-horizon coding agents.
What is AI coding agent reliability?
AI coding agent reliability is the ability of a coding agent to complete software tasks while staying within engineering constraints. Reliable agents finish tasks, run relevant tests, limit unnecessary edits, avoid repeated failures, report progress accurately, and use tools safely inside the development environment.
Adoption is mainstream. Trust hasn’t caught up.
Companies want AI coding agents. Benchmarks are not enough. Security teams are nervous. Engineering leaders need workflow-level reliability evidence — that is the gap.
How coding agents fail — and what to watch
| Failure mode | What it looks like | Business risk | What to measure |
|---|---|---|---|
| Drift | Agent moves away from the original task | Review debt, bad architecture | Goal adherence across checkpoints |
| Churn | Edits repeatedly without progress | Wasted compute and human review | Edits without tests; repeated patches |
| Freeze | Long run with no meaningful action | Silent failure | Time-to-progress; tool inactivity |
| Skipped tests | Code changes without verification | False confidence | Tests run per checkpoint |
| Over-editing | Too many files changed | Scope creep | Files changed; diff size |
| Evasive repair | Suppresses errors instead of fixing logic | Fragile code | Patch-pattern review |
| False progress | Claims success without evidence | Bad handoff to humans | Claim / evidence match |
| Security exposure | Unsafe tool or file access | Data loss, credential leakage | Permissions; tool calls; secrets access |
A patch can pass a narrow test and still be bad engineering. A final benchmark score cannot show whether the agent churned, skipped tests, over-edited, or created review burden. The signal is in the process, between checkpoints — not only in the final output.
The Checkpoint Reliability Framework
Evaluate agents at task boundaries. At each checkpoint, measure behaviour, detect failure modes, and decide whether to continue, intervene, redirect, or stop the run.
Convergence
Test behaviour
Churn
Scope control
Recurrence
Claim / evidence
Early findings — pilot scale, reported honestly
Exploratory, small-n, not confirmatory. The negative results are part of the point.
Reflection didn’t constrain the next agent
Heavy steering can break convergence
Lighter, method-framed steering looked more promising
Iboga — Phase 2 Findings
A curated, pilot-scale sample of the research: the gate analysis, the correction-vs-reflection result, the convergence-break finding, the steering-format experiment, a literature review, and the evidence appendix. Honest, small-n, including the negative results.
Download the findings pack (.zip)No signup. ~90 KB. Pilot-scale & exploratory.
AI Coding Agent Reliability Audit
We evaluate how coding agents behave inside your real engineering workflow — task completion, test behaviour, churn, edit scope, repeated failures, progress accuracy, and tool/security exposure — and return a practical reliability report with failure modes, risk areas, and checkpoint recommendations.
What is AI coding agent reliability?
AI coding agent reliability is the ability of a coding agent to complete software tasks while staying within engineering constraints. Reliable agents finish work, run relevant tests, limit unnecessary edits, avoid repeated failures, report progress accurately, and use tools safely inside the development environment.
Why are benchmarks insufficient for coding-agent adoption?
Benchmarks can show whether an agent solves selected tasks, but not how it behaves inside a real engineering workflow. Teams still need process-level evidence: test behaviour, edit scope, repeated failures, security exposure, and review burden.
What is checkpoint steering?
Checkpoint steering is a workflow for controlling AI coding agents during long tasks. The agent is paused at task boundaries, its behaviour is measured, failure patterns are diagnosed, and the next step is adjusted before it continues.
What should CTOs measure before scaling coding agents?
Task completion, test execution, edit scope, churn, repeated mistakes, progress accuracy, security exposure, rollback safety, and human review burden — the metrics that reveal whether agents improve throughput or quietly create engineering debt.