Your agent demo worked. Now make it reliable.
The infrastructure between a model and a product.
Model = CPU. Context = RAM. Harness = OS.
Agent Harness v2.1
Guardrails ONTracing ON
orchestrate tool calls
Agent Task Log
Harness Control Plane2h 14m
Context
42%
Lifecycle
RUNNING
Tool Calls
12
Approvals
clear
Live Trace847 entries
14:23:01tool_call: fetch_data → 200 OK
14:23:02context: 42% → compaction skipped
14:23:03lifecycle: RUNNING
14:23:04guardrail: check passed
14:23:05checkpoint: state saved
14:23:06tool_call: process → running
14:23:07trace: decision logged
Agent calls 12 tools in sequence. Tool #7 times out. The harness retries with backoff, skips non-critical tools, and completes the workflow with a degraded-but-valid result.
Six harness capabilities
The infrastructure that turns an AI model into a production system.
Context is the new programming
Compaction, state offloading, task isolation. Your agent forgets nothing and runs forever.
Semantic compaction — 20x reduction
State offloading to disk
Task isolation between sub-agents
Zero memory loss
Boot → Run → Fail → Learn
The harness lifecycle. Same model, better results every cycle.
Every session starts with context curation
The harness loads relevant state, primes the model with domain knowledge, and establishes boundaries. This is the boot sequence — before the agent reasons about anything, the harness decides what it knows.
1
Load session state from last checkpoint2
Prime context with domain-specific knowledge3
Set tool permissions and filesystem boundaries4
Initialize prompt presets and guardrail rulesFrequently asked questions
The model is commodity. The harness is moat.
See what a production harness looks like. SimpleFunctions runs 10 prediction market theses 24/7 with automatic context management, failure recovery, and human oversight.
24/7
Autonomous monitoring
3,000+
Evaluations per thesis
0
Unrecovered crashes