AI agents behave more like distributed systems than chatbots.
Infrastructure research for:
- runtime continuity
- runtime reliability
- replayability
- recoverability
- runtime observability
- multi-agent coordination
- eval-driven governance
The focus is not model capability.
The focus is:
Can AI agents run reliably for hours, days, or weeks in real-world environments?
Most of the current AI ecosystem focuses on:
- prompting
- orchestration
- model capability
- offline eval
- memory retrieval
But production agents fail somewhere else.
They fail during runtime.
Typical failures:
- context collapse
- retry storms
- goal drift
- state corruption
- replay mismatch
- memory poisoning
- recovery inconsistency
- long-session instability
- multi-agent divergence
This repository studies that missing layer.
LLM Capability
↓
Agent Workflow
↓
Multi-Agent Coordination
↓
Runtime State
↓
Continuity / Recovery / Replay
↓
Reliability / Observability / Eval
Drift = Goal Alignment Failure
More specifically:
Drift is a runtime continuity failure caused by goal-state divergence.
The goal is not chat memory.
The goal is:
Recoverable working state.
A demo agent and a production agent are fundamentally different systems.
A demo agent:
- runs for minutes
- executes happy paths
- tolerates hidden instability
A production agent:
- runs for hours or days
- survives interruption
- coordinates across tools and agents
- maintains continuity under changing environments
- supports replay and debugging
- requires observability and governance
The hardest problems are no longer:
- text generation
- tool calling
- prompt engineering
The hardest problems are becoming:
- runtime durability
- continuity semantics
- replayability
- recovery guarantees
- state consistency
- runtime eval
Recover interrupted long-running execution.
Capture runtime events, transitions, failures, and recovery paths.
Persist recoverable runtime state instead of raw conversation history.
Replay failed trajectories for debugging and regression analysis.
Measure continuity stability, recovery quality, and runtime drift behavior.
Detect divergence between current execution state and original goal state.
Study state consistency and orchestration failure patterns across agent swarms.
Observed runtime failure patterns currently being tracked:
| Failure Type | Description |
|---|---|
| scope_expansion | Agent silently enlarges task scope |
| goal_forgetting | Original goal disappears from execution focus |
| cleanup_spiral | Endless cleanup/refactor behavior |
| dependency_cascade | Dependency upgrades trigger unrelated repair chains |
| exploratory_loop | Exploration without convergence |
| autonomous_refactor | Large refactor initiated without authorization |
| replay_mismatch | Replayed execution diverges from original trajectory |
| hallucinated_state | Agent invents nonexistent runtime state |
This taxonomy evolves together with real runtime sessions.
agent-runtime-quality/
├── docs/
│ ├── concepts/
│ │ ├── runtime-continuity.md
│ │ ├── state-drift.md
│ │ ├── replayability.md
│ │ └── recoverability.md
│ │
│ ├── architecture/
│ │ ├── runtime-event-model.md
│ │ ├── checkpoint-design.md
│ │ ├── recovery-pipeline.md
│ │ └── multi-agent-coordination.md
│ │
│ ├── runtime-failures/
│ │ ├── context-collapse.md
│ │ ├── cleanup-spiral.md
│ │ ├── dependency-cascade.md
│ │ └── replay-mismatch.md
│ │
│ ├── engineering-notes/
│ │ ├── runtime-wars-2026.md
│ │ └── distributed-systems-for-agents.md
│ │
│ ├── eval/
│ │ ├── replay-benchmark.md
│ │ ├── drift-fixtures.md
│ │ └── runtime-governance.md
│ │
│ └── roadmap/
│
├── examples/
└── drafts/
This repository is positioned around:
- Agent Runtime
- Runtime Reliability
- Runtime Continuity
- Runtime Observability
- Runtime Eval
- Runtime Governance
- Multi-Agent Runtime Systems
This project is NOT:
- another chatbot wrapper
- another prompt playground
- another vector database
- another generic agent framework
The direction is closer to:
Runtime systems research for autonomous AI agents.
A dedicated runtime drift prototype is being developed separately:
Repository:
https://github.com/hugfeature/drift
Current capabilities:
- drift timeline visualization
- runtime narrative reconstruction
- goal lifecycle tracking
- replay-oriented debugging
- human takeover recommendation
- runtime eval fixtures
One important lesson from the prototype:
Synthetic traces are easy.
Real autonomous runtime failures are not.
Current priorities:
- Real long-running runtime session collection
- Runtime drift evaluation fixtures
- Replay and trajectory debugging
- Multi-agent failure analysis
- Runtime governance signals
- Human takeover recommendation quality
- Runtime durability semantics
The focus is no longer building agent demos.
The focus is building:
Reliable runtime infrastructure for autonomous agents.
Adjacent systems and inspirations:
- LangSmith
- OpenTelemetry
- Arize Phoenix
- OpenAI Evals
- Temporal
- Distributed systems observability tooling
The goal is not to replicate them.
The goal is to explore:
- runtime continuity
- runtime recovery
- runtime drift
- replayability
- agent reliability
as first-class infrastructure problems.
Active runtime research and engineering exploration.
Core drift analysis pipeline is operational.
Current bottlenecks:
- real autonomous runtime sessions
- believable failure trajectories
- eval-quality runtime fixtures
- recovery benchmarking
- runtime governance thresholds
The runtime architecture, terminology, and taxonomy are evolving together with the agent ecosystem.