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Agent Kernel Architecture ​

Secure, Deterministic Execution of Distributed Compute Workloads ​

The Agent Kernel is the execution subsystem running inside every Forge Agent.
It is responsible for securely processing compute shards dispatched by the Hub, producing deterministic partial results, and streaming them back for aggregation.

Agents run on heterogeneous hardware across the planet — laptops, servers, cloud nodes — yet must deliver consistent and reproducible execution.

The Agent Kernel ensures this through isolation, deterministic kernels, structured outputs, and strict communication protocols.

1. Responsibilities of the Agent Kernel ​

1.1 Execute Compute Shards ​

Run CPU/GPU kernels defined by the adapter (Monte Carlo, PCA, BLAS, FFmpeg, etc.).

1.2 Enforce Deterministic Behavior ​

Guarantee predictable results through:

  • seeded RNG streams
  • consistent floating-point behavior
  • structured output formatting
  • adapter-level deterministic kernels

1.3 Sandbox Isolation ​

Prevent workloads from interfering with the host system:

  • restricted file system access
  • memory boundaries
  • controlled process execution
  • configurable GPU access

1.4 Streaming Results to Hub ​

Send partial results back using QUIC streams with:

  • backpressure handling
  • resumability
  • structured metadata

1.5 Verification Support ​

Return additional metrics so the Hub can:

  • validate shard correctness
  • detect malicious or inconsistent outputs

1.6 Self-Update and Lifecycle Management ​

Gracefully apply updates pushed by the Hub without interrupting compute flows.

2. Internal Kernel Architecture ​

           ┌─────────────────────────┐
           │     Agent Process       │
           └─────────────┬───────────┘
                         ↓
      ┌─────────────────────────────────┐
      │         Agent Kernel            │
      └────────────────┬────────────────┘
                       ↓
   ┌───────────────────────────────────────┐
   │            Execution Engine           │
   │   - CPU kernel dispatch               │
   │   - GPU kernel dispatch (optional)    │
   │   - adapter-specific runtime          │
   └────────────────┬──────────────────────┘
                    ↓
        ┌──────────────────────────┐
        │     Sandbox Layer        │
        └──────────────────────────┘
                    ↓
        ┌──────────────────────────┐
        │    Result Formatter      │
        └──────────────────────────┘
                    ↓
  ┌───────────────────────────────────────────┐
  │ QUIC Stream Handler (bidirectional)       │
  └───────────────────────────────────────────┘

3. Execution Flow ​

Step 1 — Shard Received ​

Hub sends a structured work unit:

json
{
  "job_id": "risk.2025-12-01",
  "shard_id": 14,
  "kernel": "montecarlo",
  "params": {...},
  "seed_offset": 1440000
}

Step 2 — Kernel Evaluation ​

Agent selects appropriate kernel:

  • montecarlo_run()
  • blas_tile_multiply()
  • pca_sample_member()
  • ffmpeg_segment_transcode()

Step 3 — Isolation ​

Execution occurs inside an isolated context:

  • no external network access
  • no arbitrary file writes
  • memory quota enforced
  • CPU/GPU limits optional

Step 4 — Partial Result Construction ​

Agent returns structured output:

  • scalar values
  • arrays
  • histograms
  • matrix tiles
  • media chunks
  • metadata

Step 5 — QUIC Return Stream ​

Results are streamed with:

  • ACK logic
  • congestion control
  • retry behavior
  • compression where applicable

4. Deterministic Compute Guarantees ​

To ensure reproducibility:

• Seeded RNG Streams ​

Each shard receives a deterministic seed offset.

• Deterministic Kernel Algorithms ​

Adapter kernels are written to avoid nondeterminism.

• Stable Floating-Point Patterns ​

Consistent rounding and evaluation order are enforced.

• Structured Output ​

Avoids order-dependent noise.

This allows:

  • regulated industries to rely on results
  • reruns to produce identical outputs
  • cross-system auditing

5. Sandbox & Security Model ​

Agents enforce local sandboxing:

RestrictionPurpose
Limited filesystemPrevents data exfiltration
No external HTTPPrevents side-channel leaks
Memory ceilingsAvoids host instability
Execution quotasAvoids long-running jobs
GPU access controlPrevents unauthorized acceleration

Hub never sends:

  • credentials
  • internal secrets
  • cross-tenant data

Agents execute only pure compute kernels.

6. Performance Considerations ​

Agents report:

  • wall time
  • compute time
  • bandwidth usage
  • error counts
  • verification metrics

Scheduler uses this data to:

  • rerank agents
  • rebalance workloads
  • identify unreliable nodes

Performance Variability ​

Heterogeneous hardware is normalized using shard sizing algorithms to maintain consistent job-level timing.

7. Failure Handling ​

Agent Kernel automatically handles:

• Internal Kernel Panic ​

Shard aborted → Hub reassigns.

• Timeout ​

Shard exceeded expected wall time → Kill + resubmit.

• Verification Miss ​

Results fall outside expected numerical range → Agent downscored.

• Transport Failure ​

QUIC reconnect → stream resume → continue.

No single Agent failure affects job correctness.

8. GPU Execution Path ​

If GPU is available and the kernel supports it:

  • GPU context is initialized
  • kernel dispatched to CUDA/OpenCL layer
  • memory is pinned and streamed
  • fallbacks triggered for unsupported ops

GPU acceleration is used for:

  • BLAS/MatMul
  • high-resolution FFmpeg
  • certain PCA operations

9. Upgrade Path (Self-Update) ​

Agent performs:

  1. Download → verify → apply update
  2. Validate new version
  3. Restart kernel
  4. Resume operation

Updates are cryptographically signed and rollback-safe.

Related Documentation ​