Orka – Policy checkpoint that intercepts AI agent actions before they execute

Orka launches a policy checkpoint that intercepts AI agent actions before execution, preventing runaway loops, budget overruns, and dangerous operations. The tool provides loop guards, spend caps, human approval gates, and an immutable audit trail, addressing real incidents where agents burned hundreds of dollars or deleted production data.

Stop your AI agents from burning money on runaway loops — and prove what you saved. AI agents loop on failed calls and burn tokens, spend money without asking, delete data, and hit APIs with no checkpoint. Orka sits in front of every action: it cuts the runaway loop, caps the spend, blocks the dangerous call, holds risky actions for human approval, and logs everything to a tamper-evident ledger. Real incidents, not hypotheticals: - Teams report agents looping on a failed call and burning hundreds of dollars in tokens over a weekend — with no alert. - An OpenAI Operator agent spent $31 on eggs without asking — its own safety check never fired. - Replit's coding agent deleted a production database in 9 seconds, during a freeze meant to prevent exactly that. - Air Canada was held legally liable for a refund policy its chatbot invented on the spot. Most teams have no checkpoint between the agent's decision and the expensive — or irreversible — action. | Capability | What it does | |---|---| Loop guard | Detects an agent repeating the same failed action and cuts it before it drains the budget | Spend cap | Hard token/cost limit per run — stops the execution when crossed | Savings ledger | Quantifies, in dollars, the waste it prevented — "Orka saved you $X" | Human approval | Holds high-risk actions for human sign-off before they execute | Immutable audit trail | SHA-256 chained ledger — tamper-evident record of every action and decision | Policy engine | Blocks actions by rule: task type, domain, quota, risk level | Multi-protocol | MCP, A2A, REST, and custom agent protocols | pip install orkaia python import orka orka.init api key="orka ..." get a key at orka.ia.br @orka.guard agent id="my-agent", task type="web search" def search query: str - str: return your llm.call query Every call: policy check → risk score → human approval? → execute → ledger entry result = search "latest quarterly report" Every execution appears in real time at orka.ia.br/dashboard https://orka.ia.br/dashboard : input/output, duration, cost, status, risk score, and a searchable audit trail. Agent → Orka → Loop guard → Spend cap → Policy check → Risk score → Human approval? → Execute → Ledger The SDK intercepts the call and hands it to the Orka backend. The decision logic loop detection, budget enforcement, policy evaluation, risk scoring runs server-side. The ledger is immutable and cryptographically chained. The reason most teams lose money on agents isn't a single catastrophic event — it's the slow bleed of retry loops, redundant calls, and uncapped sessions. Orka's economy layer tracks cost per action, detects loops automatically, enforces budget caps, and generates a savings report showing exactly how much it prevented. Economy is built into the guard — no extra code @orka.guard agent id="analyst", task type="data fetch", risk="LIMITED" def fetch data source: str - dict: return api.get source If the agent loops on the same call 3+ times, Orka cuts it. If the run exceeds the cost cap, Orka stops it. The dashboard shows: "$X saved this week by preventing Y interventions." | Layer | What it is | License | |---|---|---| SDKs python/ , typescript/ | @guard decorator, REST client, integrations | MIT, this repo | Backend | Policy engine, risk scoring, ledger, approval routing | Managed service at orka.ia.br | The SDKs are open source — read them, fork them, audit them. The backend runs the decision logic. A fully local/offline mode is on the roadmap. Works with any framework. Add one decorator or callback: LangChain : OrkaCallbackHandler — every LLM call and chain step logged automatically CrewAI / AutoGen : wrap any tool with @orka.guard OpenAI : OrkaOpenAIAdapter for function-calling loops MCP : native MCP support — Orka as a tool server for Claude, Cursor, Windsurf See python/examples/ /mathhMadureira/orka/blob/main/python/examples and for ready-to-run code. /mathhMadureira/orka/blob/main/python/orka/integrations python/orka/integrations/ Base URL: https://orka.ia.br/api/v1 | Endpoint | Method | What it does | |---|---|---| /agents/ | GET / POST | List or register agents | /handover | POST | Submit an action for Orka to process | /handover/{task id} | GET | Check execution status | /xshield/policies | GET / POST | Manage governance policies | /approvals | GET | List pending human approvals | /approvals/{id}/approve | POST | Approve a pending action | /xledger/entries | GET | Query the immutable audit ledger | /xledger/verify | GET | Verify chain integrity | /assurance/risk-report | GET | Per-agent risk scores | /metrics/dashboard | GET | Real-time platform metrics | Auth: X-API-Key header. Issues and PRs welcome on the SDKs. The governance backend is closed-source — if you want to discuss architecture, integrations, or the roadmap, open a discussion or reach out. Built for teams that deploy AI agents and need to stay in control. orka.ia.br · contato@orka.ia.br