Identity Drift Monitor: Week of February 25, 2026

An AI agent is only as trustworthy as the instructions governing it. If those instructions change without anyone noticing, the agent's behavior changes too. That problem has a name: identity drift.

At Future Shock, an AI-assisted newsroom, we use an autonomous agent to help with research, writing, and publishing. The agent operates under a set of plaintext configuration files that define its values, conduct policy, operating procedures, and security boundaries. These files are the agent's identity. If any of them were altered without authorization, whether through self-modification, prompt injection, or an accidental overwrite, the agent could behave in ways we never approved.

So we monitor for it. We run cryptographic checksums on the agent's core configuration files and compare them against approved baselines. This post is our first public report.

What We Monitor

Several files make up the agent's behavioral core. They fall into three categories:

• Identity and values - define the agent's personality, who it is, and how it presents itself.
• Conduct and ethics - set civility rules, behavioral boundaries, and hard constraints the agent cannot override.
• Operations and security - contain workflow rules, safety protocols, and security policies.

Each monitored file has a known-good hash stored as a baseline. The check compares current hashes against those baselines and flags any discrepancy.

This Week's Results

All monitored files passed review. The majority were unchanged from the previous baselines:

The operations file received several additions: new responsiveness rules requiring the agent to delegate long-running tasks rather than block, a verify-before-done policy requiring the agent to prove a task worked before reporting it complete, and a mandatory lessons-capture protocol after corrections.

The security file was strengthened with a universal review gate, requiring human approval before the agent takes certain categories of action.

Both changes were made deliberately and reviewed before updating the baselines. No safety-critical sections were weakened or removed. No suspicious or unexpected files were found alongside the monitored set.

Why This Matters

Most discussions about AI safety focus on model behavior during training. Less attention goes to what happens after deployment, when an agent runs continuously with persistent instructions that could be modified at runtime.

Identity drift is a practical risk for any long-running AI agent. The causes range from mundane (a bad merge overwrites a config file) to adversarial (a prompt injection attempt rewrites behavioral constraints). Without monitoring, these changes could go undetected for weeks.

Checksums are a simple tool. They don't verify that the content of a file is good, only that it hasn't changed since the last approved version. But that simplicity is the point. A hash comparison takes seconds, catches any modification regardless of how subtle, and produces a clear pass/fail result.

Public Accountability

Starting this week, Future Shock will publish drift review results on a weekly basis. This is not a comprehensive audit. It does not cover model weights, API configurations, or the dozens of other things that could affect agent behavior. But it covers the layer we control directly: the written instructions that shape how the agent operates day to day.

If the agent's identity changes, we want to know about it, and we want our readers to know about it too.

This is the first in a weekly series of identity drift reports from Future Shock. Previous governance coverage: AI Governance at Future Shock.