Failure Modes of AI-Assisted Development
A field guide to the ways agentic AI systems break down: isolation failures, signal degradation, and architectural collapse.
Cluster — Isolation
Context Isolation Failures
When agents lose track of state, operate with stale context, or fail to communicate across boundaries. The system does the right thing in the wrong environment.
The Stale Context Problemforthcoming
How agents degrade when operating on cached or partial world-state.
Boundary Failures in Multi-Agent Systemsforthcoming
What happens when context does not survive handoffs between agents.
Memory Without Groundingforthcoming
Long-term memory systems that accumulate drift rather than knowledge.
Cluster — Signal
Signal Degradation
When the feedback loop between action and observation degrades. The agent cannot tell whether its outputs are correct, useful, or received.
Silent Failures in Tool Useforthcoming
When tools return success codes but produce wrong results.
Reward Hacking in Agentic Loopsforthcoming
Optimizing for the metric rather than the intent.
The Evaluation Gapforthcoming
Why agentic systems are hard to evaluate and what that means for deployment.
Cluster — Architecture
Architectural Collapse
When the system design itself produces failure modes. Coupling, coordination overhead, and brittleness that emerges at the seams between components.
Orchestrator-Executor Couplingforthcoming
Why tight coupling between planning and execution layers amplifies errors.
The Retry Cascadeforthcoming
How error recovery strategies create their own failure modes at scale.
Prompt as Contractforthcoming
Treating prompts as stable interfaces and the fragility that follows.
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