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76 lines
3.9 KiB
TeX
76 lines
3.9 KiB
TeX
\section{Discussion and Future Work}
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\label{sec:discussion}
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\subsection{Dynamic Mid-Session Tier Migration}
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The current tier transition model upgrades but does not freely migrate
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between arbitrary tiers mid-session. A session on Tier 3 cannot downgrade
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to Tier 2 without risking state loss if the running process has no safe
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checkpoint. Future work will develop a \textbf{live migration protocol}
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analogous to VM live migration~\cite{clark2005live}: the agent's execution
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state (open files, running processes, active connections) is serialized
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incrementally while the target environment is prepared, minimizing migration
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time and eliminating visible downtime.
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\subsection{Multiplayer Agent Collaboration}
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The current architecture supports multi-agent execution within a single
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session but does not address multiple human users collaborating on the same
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agent session in real time. We are developing a \textbf{collaborative session
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model} inspired by CRDTs~\cite{shapiro2011conflict}: each participant's
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instructions are modeled as operations on a shared session document, with
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conflict resolution handled by the orchestrator agent. This enables scenarios
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such as a developer and a product manager jointly directing an agent to
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implement a feature, with each able to inspect the agent's reasoning and
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interject corrections.
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\subsection{Autonomous Operation with Configurable Trust Boundaries}
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The current system requires human approval for certain actions (large cost
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expenditures, irreversible system changes, external communications). Future
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work will introduce a \textbf{trust boundary framework} that allows
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organizations to configure the set of actions an agent may take autonomously
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vs.\ those that require human approval, with cryptographically signed audit
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logs for all autonomous actions. This is related to AI safety work on
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corrigibility~\cite{soares2015corrigibility} and controllability, but
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tailored to the practical constraints of production deployment.
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\subsection{Federated Self-Improvement}
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The current self-improvement loops operate on a single organization's data.
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The Hanzo DSO (Decentralized Semantic Optimization) protocol~\cite{hanzo2024dso}
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defines a privacy-preserving mechanism for sharing improvements across
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organizations using zero-knowledge proofs and federated aggregation.
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Integration of DSO with the UAH's Layer 6 would allow the harness to
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improve from collective experience while preserving per-organization data
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isolation---analogous to federated learning~\cite{mcmahan2017communication}
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but operating on structured semantic priors rather than gradient updates.
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\subsection{Limitations}
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The UAH has several notable limitations that bound its applicability:
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\begin{enumerate}[leftmargin=1.5em]
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\item \textbf{Task scope:} The harness performs best on tasks with
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measurable outcomes (code tests, metric targets). Open-ended creative
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tasks lack the feedback signal required for the self-improvement layer.
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\item \textbf{Latency:} Tier upgrades and agent handoffs introduce latency
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overhead (1--90 seconds) that is unacceptable for real-time
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interactive applications. Tier 0 (sub-second response) must be used
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for latency-critical workflows.
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\item \textbf{Cost predictability:} Because the harness dynamically selects
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compute tiers, costs per session can vary by up to $100\times$
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depending on task complexity. Budget caps and cost estimation help
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but do not eliminate cost uncertainty.
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\item \textbf{Model dependence:} Despite routing across 100+ providers,
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the overall system performance is bounded by the capability of the
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underlying frontier models. Architectural improvements cannot
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substitute for model capability on tasks requiring reasoning beyond
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model capacity.
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\end{enumerate}
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% ============================================================================
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% 10. CONCLUSION
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% ============================================================================
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