Agentic AI systems are hitting a wall: 40% of current projects will be canceled by 2027 due to runaway costs, unclear ROI, and lack of sustained agency across multi-day, multi-system workflows.
JouleWork reframes the problem: treat agent execution as a thermodynamic process. Every action has a measurable cost (compute, latency, risk). The core loop continuously reallocates effort so expected utility exceeds cost. Actions below that threshold get deferred or escalated.
Architecture is split into two layers:
1. Procedural execution: routing tickets, coding invoices, extracting clauses with confidence scores, generating reports.
2. Governed knowledge: policy citations, prior decisions with receipts, pattern recognition from internal docs.
Both layers run on-prem or in your own cloud. Zero data retention by model providers. Access follows existing identity rules. Every action leaves an audit trail that can be reversed.
Orchestration is narrow and scheduled. The agent executes defined steps, measures utility delta, then continues or escalates. Policies are platform-enforced. Trust comes from visibility into energy and outcome per action, not blind model autonomy.
Model selection is dynamic: simple extractions route to cheaper/faster models, harder classifications use stronger ones. Humans intervene only when error cost exceeds review cost.
The survivors by 2027 won't be the ones chasing AGI theater. They'll be the ones who optimized for bounded utility under real constraints from day one.
JouleWork reframes the problem: treat agent execution as a thermodynamic process. Every action has a measurable cost (compute, latency, risk). The core loop continuously reallocates effort so expected utility exceeds cost. Actions below that threshold get deferred or escalated.
Architecture is split into two layers:
1. Procedural execution: routing tickets, coding invoices, extracting clauses with confidence scores, generating reports.
2. Governed knowledge: policy citations, prior decisions with receipts, pattern recognition from internal docs.
Both layers run on-prem or in your own cloud. Zero data retention by model providers. Access follows existing identity rules. Every action leaves an audit trail that can be reversed.
Orchestration is narrow and scheduled. The agent executes defined steps, measures utility delta, then continues or escalates. Policies are platform-enforced. Trust comes from visibility into energy and outcome per action, not blind model autonomy.
Model selection is dynamic: simple extractions route to cheaper/faster models, harder classifications use stronger ones. Humans intervene only when error cost exceeds review cost.
The survivors by 2027 won't be the ones chasing AGI theater. They'll be the ones who optimized for bounded utility under real constraints from day one.