Runaway inference cost
Agents resend context, tool schemas, logs, and repeated summaries across many model calls. The cost grows with every loop.
The agent cost problem
Most agents use AI where software is enough.
Modern agents are powerful, but their default loop is expensive: reason, call a tool, reason again, retry, summarize, validate, and repeat. Many of those steps can be compiled into deterministic work that is cheaper, faster, and easier to audit.
Unfocused execution
Broad tool access and loose prompts make agents wander through files, APIs, retries, and irrelevant context.
Weak production control
Without deterministic gates, side-effect policies, and replayable traces, teams cannot safely scale agentic workflows.
The compiler loop
Observe the agent. Compile the waste out.
The system works like profile-guided optimization for software. First it scans the original agent locally, redacts sensitive material, and creates a diagnostic bundle. The cloud compiler then builds a process graph, identifies waste, emits a hybrid pipeline, and sends it back for local verification against the baseline.
01
Scan locally
Run pipit scan inside the customer workspace. The collector records file structure, prompts, tool surfaces, coarse symptoms, and optional traces without exporting credentials.
02
Inspect before upload
The CLI shows exactly what leaves the machine: hashes, redacted snippets, aggregate metrics, omitted paths, and safety status. The free scan shows symptoms, not the optimization recipe.
03
Spend credits to compile
Paid cloud compile turns the safe bundle into an executable recipe: deterministic steps, focused LLM calls, cache boundaries, validation gates, and a package the customer can apply locally.
04
Verify locally, then promote
The optimized artifact is created beside the original agent. The customer runs equivalence checks and shadow runs before promotion; rollback stays under customer control.
4x
Typical speed target for repetitive agent workflows
70%
Target reduction in unnecessary model calls
0
Critical side-effect policy violations allowed
1 IR
One versioned pipeline spec for every deployment
Production output
A deployable runtime, not just advice.
The paid output is a versioned hybrid workflow with code nodes, retrieval nodes, validation nodes, focused LLM calls, human gates, and fallback. It is designed to run inside the customer's workspace first, then graduate to private cloud or managed deployment when trust is earned.
pipeline.yaml
validated candidate
pipeline:
id: coding_agent_compiled
source_agent: original_agent_001
policies:
max_cost_usd: 0.50
external_mutations: approval_required
fallback: original_agent
nodes:
- id: collect_context
type: code
implementation: repo_context_selector
- id: classify_task
type: llm
model_policy: cheap
tools: []
- id: parse_test_log
type: code
implementation: failure_block_parser
- id: generate_patch
type: llm
model_policy: strong
tools: [read_file, write_patch]
- id: validate
type: validation
validators: [schema, tests, policy]
fallback:
trigger:
- validation_failed_after_repair
- confidence_below_thresholdCredit-priced compile
Free local scan, explicit estimate, reserved credits, and charged compile.
Versioned pipeline IR
Diffable workflow spec with explicit inputs, outputs, and policies.
Runtime package
Pipeline, runtime config, apply plan, verification plan, and rollback guidance.
Validation gates
Promotion rules that block unsafe or lower-quality candidates.
Runtime monitoring
Cost drift, quality drift, fallback rate, and cache hit rate.
Enterprise posture
Built for agents that touch real systems.
The platform assumes customer data, secrets, internal APIs, and side effects are sensitive from day one. The default alpha flow keeps scans, credentials, real data, verification, promotion, and rollback on the customer side while the proprietary compiler runs in our cloud.
Transparent local collector
Inspect every field before upload; the local tool collects evidence, not the recipe.
Secrets isolation
Keys, databases, CRM access, and raw private data stay out of the cloud compiler.
Credits and limits
Every paid compile has an estimate, max-credit guard, reservation, and ledger entry.
Audit trail
Track account sessions, bundle fingerprints, compile jobs, artifacts, and credit spend.
Framework-neutral
Works above the agent stack.
Agent Compiler is designed to be invoked by the AI coding agent the user already trusts. Codex, Claude Code, Cursor, Devin, or a custom runner can call the CLI, inspect the bundle, request compile, apply the artifact, and run local verification.
OpenAI Agents
LangGraph
CrewAI
AutoGen
Google ADK
MCP
OpenTelemetry
DSPy
Langfuse
Phoenix
Temporal
Custom Agents
Bring one agent. Get a locally verified optimized package.
Start with a single expensive agent. The free output is a safe diagnostic bundle and savings signal. The paid output is a compiled package your team can verify beside the original before promotion.