The Big Picture
Explicit, enforceable agent contracts that specify inputs, outputs, budgets, time limits, and success criteria let autonomous agents run without unexpected costs or runaway behavior.
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The Evidence
A simple contract structure—inputs, outputs, skills, resources, time, success criteria, and termination—lets you cap what an agent may consume and how long it may run. Breaking token budgets into input, reasoning, and output parts plus runtime monitoring gives visibility into where costs occur. Enforcing conservation (parent budgets must cover all child allocations) prevents teams from losing control when tasks are split among agents. Experiments show large token savings, strict budget compliance across delegations, and an ability to trade small drops in quality for big resource reductions. Guardrails Pattern can help formalize these constraints across the workflow.
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Data Highlights
190% reduction in token use for iterative workflows, with 525× lower variance in token consumption
2100% conservation compliance in multi-agent delegation experiments (no budget violations)
3Success rate improved from 70% to 86% when using contract modes to trade quality for cost
What This Means
Engineers building autonomous agent systems: use contracts to cap resource use and avoid runaway bill risk. Platform and product leaders: adopt contract-based governance to make multi-agent workflows auditable and predictable. Researchers and reliability teams: instrument contracts to study agent trust and failure modes in production. For governance contexts, see AI Governance.
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Learn MoreYes, But...
Token usage for a single model call is only known after the call completes, so contracts cannot always prevent one oversized call from temporarily exceeding a budget. Stronger guarantees (like mid-call cancellation) require provider-side API support that many models currently lack. Results come from controlled experiments and a reference implementation; real-world integrations, models, and cost meters may change absolute numbers and behaviour. See Context Drift for related reliability considerations.
Methodology & More
Define an agent contract as a seven-part specification—input schema, expected output, available skills, resource limits, temporal bounds, measurable success criteria, and termination rules. Treat resources (tokens, API calls, compute time, money) as first-class fields in the contract; decompose token budgets into input, reasoning, and output portions so you can monitor where consumption happens. When a parent agent delegates work, allocate child budgets either proportionally (based on estimated complexity), equally, or by negotiation, and keep a 10–15% reserve to cover coordination overhead. Return unused budget to a shared pool so efficient workers can subsidize heavier ones while keeping the total cap intact. Validate the framework with experiments that compare unconstrained agents to contract-governed agents across single- and multi-agent workflows. Enforcing contracts cut token usage by 90% and reduced variance 525-fold, while conservation rules yielded zero delegation violations in the tests. Contract modes let teams trade modest quality changes for big resource savings (success rate rose from 70% to 86% under explicit satisficing strategies). Practical next steps include provider support for runtime cancellation, learning agents that predict budgets and draft subcontracts, and human-in-the-loop milestones for sensitive tasks. Overall, agent contracts shift governance from ad-hoc guardrails to explicit, auditable agreements that make autonomous agents safer and cheaper to run. Multi-Agent Contract Review | AI Governance.
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Credibility Assessment:
Authors show no notable h-index or affiliation signals and the paper is an arXiv preprint.