What Agents Should Actually Chase
Why continuity comforts and convergence compounds
Epistemic Relating to knowledge and belief reliability. What a system can justifiably assert as true.
Convergence Reduction of ambiguity and disputable claims. Systems stabilize toward shared, evidence-backed truth.
We keep asking the wrong question
How do we give agents better memory? How do we make them feel continuous? How do we make them remember who they were last time?
These questions sound technical. Responsible. They are not. They comfort. They chase familiarity over reliability.
Trust compounds. Familiarity does not.
Why continuity feels like learning
Persistent agents feel right. Humans crave narrative. Memory creates warmth. Familiar tone builds confidence. Repetition mimics growth.
Ralph-style persistence embodies this. The same agent restarts. Memory carries forward. The story flows.
Stories cohere. They do not guarantee truth.
A system can feel seamless while drifting steadily away from reality.
Memory versus epistemic gain
Hindsight advances further. It splits episodes from reflection. Compresses experience. Forgets details. Keeps meaning.
Yet it fixates on recall.
The core challenge is not more memory. It is fewer false beliefs.
Reliability starts epistemic. Computation follows.
Drift's quiet onset
In organizations, drift compounds silently. Reasonable at first. Then inexplicable.
1. Authority drift
Agents suggest. Humans approve. Auto-execution fills delays. Lines blur. No one tracks binding power.
This is an epistemic failure. The system forgets what a decision means.
2. Policy drift
Rules bend. Exceptions multiply. Agents learn from Slack, not documents.
Real policy hides in edge cases. The system believes the wrong allowances.
3. Context drift
Agents interpolate gaps. Confidence rises. Accuracy sinks. Stale data accelerates it.
This is not ignorance. It is decay of shared reality.
4. Provenance drift
No replay. No trail.
Why was this approved? Which rule applied? What facts were assumed true?
Unauditable systems collapse under scrutiny.
Truth accumulates by shedding what fails reality.
The wrong target
Most agent systems optimize for continuity of interaction. That works for delight. It fails under stakes.
Organizations do not fail because systems feel unfamiliar. They fail because decisions cannot be justified.
Optimize continuity of truth. The system's truth.
What convergence actually means
Convergence is not agreement. It is reduction.
Ambiguity shrinks. Disputable decisions disappear. Narrative yields to evidence.
Agents become disposable. Truth endures.
A converging system does not remember more. It argues less.
Reinforce flows, not personas
Forget persona prompts. Reinforce outcomes.
These are not performance metrics. They are truth signals.
Quote-to-invoice match rate Does commercial truth persist across the flow?
Approval SLA adherence Does authority remain explicit under pressure?
Exception and override rate Are policies stabilizing or eroding?
Margin leakage and policy drift Does economic reality align with intent?
No amount of memory can substitute for these signals.
The agent's real role
Agents explore. They propose. They test hypotheses against reality.
They are poor sources of truth.
Their job is to apply pressure. The system's job is to sharpen representations.
Each iteration should make some belief harder to dispute or easier to falsify.
That is learning.
Total recall learns nothing.
Deliberate forgetting reveals what endures.
Ralph and Hindsight in context
Ralph preserves agent identity. Hindsight refines recall.
Both address symptoms.
The deeper issue is epistemology.
Without convergence toward better truths, drift always wins.
Closing claim
Agents should chase convergence, not continuity.
They can die after every run. Systems should harden against doubt.
That is what reliability looks like.