Your agent shipped. Now it has to get better.
Rayform turns production traces and outcomes into tested changes to prompts, tools, retrieval, memory, and workflows—then routes every release through your evals, policies, approvals, and rollback controls.
Observability shows what happened
- Traces: Full conversation + tool calls + latency
- Evals: Correctness, hallucination, instruction adherence
- Tickets: Customer escalations, reversions, human fixes
- Outcomes: Revenue per interaction, resolution, containment
You see the problem. You measure the cost. Then you are stuck.
Rayform prepares what should change next
- Diagnose: Cluster failure patterns from your traces
- Propose: Versioned diffs across prompts, tools, workflows
- Evaluate: Test against your evals and protected slices
- Govern: Route through approvals and rollback policies
From observation to improvement. Bounded. Governed. Reversible.
The Recursive Machine
Seven stages of governed improvement. From signal to safer change.
Observe
Capture traces, outcomes, interventions, corrections, cost, and latency.
Example Change Request ILLUSTRATIVE
v2.3 deployed to 281 requests over 4h
Measured gain: +8.3% resolution rate
Decision: RETAIN
Boundaries That Cannot Be Rewritten
Define what tools, data, and actions the agent can touch. Candidate changes must stay within this perimeter.
Your evaluation rules are enforced. A proposed change must pass your evals before proceeding to deployment.
You define risk-tier routing: low-risk changes may auto-advance; high-risk changes require human approval. All changes require human sign-off before production deployment.
Every deployment is tagged and versioned. If canary results indicate regression, changes can be reverted to the previous version.
Critical customer segments, compliance-sensitive operations, or high-value transactions are held out from canary.
Changes to one agent do not propagate to others unless explicitly versioned and re-approved.
Recursive improvement with bounded authority.
The system learns from production. The system never makes autonomous changes to its own permissions, eval criteria, or deployment policy.
The Stack Fit
Target integration contract: ingest traces and verified outcomes, evaluate candidate behavior, and emit reviewed artifacts through your existing Git and release systems. Exact connectors depend on the pilot stack.
FAQ
What can change in a proposal?
Prompts, tools, retrieval strategies, routing logic, memory retention, and workflow policies. Any aspect of agent behavior that can be versioned and tested. The change must fit within the agent's defined permissions and must pass your evals.
Who approves changes?
You define approval policy by risk tier. Changes are routed based on risk level: low-risk changes may advance with limited approval; high-risk changes require explicit human approval. The agent cannot approve its own changes.
Can I learn from other agents' changes?
Rayform tracks versioned changes across your fleet of agents. If one agent's improvement is proven, you can inspect its diff and propose similar changes to other agents—then evaluate each in isolation. Cross-agent learning is manual and governed, not automatic.
Does Rayform work with any model or framework?
Rayform is designed to work with a broad range of models and frameworks by analyzing traces and proposals. It reads execution traces (function calls, decisions, outcomes) and works with changes to prompts, tools, memory, and routing. Compatibility depends on your specific stack; contact us to discuss your setup.
What if a canary fails?
Canary results are compared against control. If key metrics indicate regression, the change is held and may be reverted to the previous version. All versions are tagged and can be reviewed. Rollback decisions involve human review.
What kind of outcome signals do you use?
Depends on the agent type. Support agents: resolution rate, CSAT, cost per interaction. Voice agents: containment, transfer rate, latency, revenue per call. Coding agents: PR acceptance, test pass rate, merge time, escaped defects. You define what success means for your domain.
Can you use Rayform for regulated industries?
Rayform is designed with governance and auditability in mind. Full versioning, approval trails, and change history support compliance workflows. Rayform can enforce domain-specific approval policies and protected evaluation slices. We recommend starting with lower-risk agents and engaging your compliance and security teams.
How do I get started?
You need production traces, measurable outcomes, and at least one evaluation suite. We'll help you connect your observability pipeline, define your approval policy, and run your first improvement loop. Contact us to discuss your needs.
Bring one production agent, existing traces, and a measurable outcome. Define the baseline, approval gates, and exit criteria before connecting production.
Evaluate one bounded workflow. Keep human approval and stop without migration if the evidence is not convincing.
Discuss a controlled pilot