Jeevs

Problem Statement


Modern AI tools suffer from three systemic problems:


Non-determinism disguised as competence


Identical prompts produce materially different outcomes, making systems untestable and unreliable.


Invisible decision-making


Tool calls, retries, hallucinations, and context collapses occur without operator awareness.


Irreversible action chains


Once an agent begins acting, it becomes difficult to interrupt, inspect, or safely roll back its behavior.


These issues make AI agents unsuitable for:


  • Infrastructure control
  • Long-running workflows
  • Safety-critical automation
  • Serious engineering environments
  • Jeevs exists to make AI boring again—in the best possible way.



Design Philosophy


Jeevs is governed by five non-negotiable principles:


  1. Determinism Over Brilliance: If a system cannot explain why it did something, it should not do it.
  2. Everything Is a Log: All actions—thoughts, retries, failures—are written to disk in human-readable form.
  3. No Hidden Autonomy:  The system never escalates its own privileges, retries silently, or expands scope without explicit instruction.
  4. Operator > Model:  The human operator is the final authority. The model is an interchangeable component.
  5. Failure Is a Feature:  Halting is success when uncertainty exceeds tolerance.



System Architecture (Conceptual)


Jeevs is structured as a layered control system:


[ Operator ]

↓

[ Command Interpreter ]

↓

[ State Validator ]

↓

[ Execution Engine ]

↓

[ Tool / Model Adapter ]

↓

[ Append-Only Log Store ]



Each layer may refuse to proceed. There is no fast path.



Core Components


1. Command Interpreter


  • Accepts explicit, bounded instructions
  • Rejects ambiguous or underspecified commands
  • Enforces scope and permissions


2. State Validator


  • Confirms preconditions before execution
  • Verifies environment invariants
  • Detects drift and halts on mismatch


3. Execution Engine


  • Executes one step at a time
  • No implicit retries
  • No parallel action without declaration


4. Model Adapter (Optional)


  • LLMs are treated as pure functions, not authorities
  • Outputs are validated like any other untrusted input
  • The system functions without an LLM at all


5. Log Store


  • Append-only
  • Human-readable
  • Replayable
  • Designed to survive system failure



Example Control Flow


Task: "Organize a project directory"


  1. Operator issues command
  2. Jeevs parses intent
  3. Preconditions checked (permissions, filesystem state)
  4. Proposed plan emitted
  5. Operator approves
  6. Single action executed
  7. Result logged
  8. System awaits next instruction
  9. At no point does Jeevs infer intent or improvise.



Failure Mode Analysis


A deliberate design constraint of Jeevs is that a single misplaced retry breaks the entire philosophy.


Why?


  • Retries imply hidden state
  • Hidden state implies unverifiable behavior
  • Unverifiable behavior implies loss of trust


Therefore:


  • No automatic retries
  • No silent fallbacks
  • No self-healing logic without explicit approval
  • Jeevs prefers to stop.




Current State (Reality Check)


  • No LLM integrated yet
  • No production users
  • No GUI
  • Operates conceptually and via manual execution


This is intentional.


The system is being proven philosophically before being expanded technically.



Why This Matters


As AI systems increasingly control infrastructure, finance, and decision-making pipelines, auditability becomes more important than intelligence.


Jeevs demonstrates that:


  • AI does not need to be autonomous to be useful
  • Safety emerges from constraints, not promises
  • Control systems deserve the same rigor as kernels and compilers



Lessons Learned


  • Most AI failures are process failures, not model failures
  • Removing features increases trust
  • Logs are more valuable than cleverness
  • Engineers want systems that can say “no”



Next Steps


  • Implement minimal LLM adapter
  • Build replayable execution harness
  • Publish open design logs
  • Dogfood Jeevs on real workflows



Closing Statement


Jeevs is an argument, expressed in code.


An argument that intelligence should be contained, inspectable, and answerable to the humans who deploy it.


If the system cannot explain itself, it does not get to act.