An agent recovers a denied synthetic insurance claim on TrueForge: it reads the claim through a local MCP connector, computes the correction in the sandbox, and pauses on a shielded tool before the only call that writes. TrueForge pauses on every shielded call, including retries. That is correct, and it is also the interesting part. Each approval references one specific pending tool call, so the same business operation can be approved twice and every yes is correct in isolation. Nothing in the approval layer relates one to another. The harness authorizes a tool call; the application cares about an operation. Different units. So the memory lives where the consequence lands. The payer mints the operation id server-side and fingerprints the exact payload at prepare time, then re-verifies that fingerprint at commit. A retry of the identical payload replays the original receipt instead of writing again. A tampered amount is refused before anything is written. That check does not depend on the model behaving well. In the demo I approve twice and the ledger holds one row, same receipt. Then I ask the agent to change the amount: it refuses on its own, and the end-to-end suite shows the payer would refuse it anyway. Three operations attempted, exactly one row written. Idempotency is not new. What is new is that the thing retrying is an agent and the thing approving is a person. Relating one approval to another is an application-side concern, confirmed as such by TrueFoundry before this was recorded. Uses MCP over Streamable HTTP, Code Mode sandbox, shielded tools, and one skill (safe-resubmission) carrying the retry policy. All data synthetic; the ledger is local SQLite with immutable rows.
Built at The Agent Harness Hackathon
Regenerate
An agent recovers a denied synthetic insurance claim on TrueForge: it reads the claim through a local MCP connector, computes the correction in the sandbox, and pauses on a shielded tool before the only call that writes. TrueForge pauses on every shielded call, including retries. That is correct, and it is also the interesting part. Each approval references one specific pending tool call, so the same business operation can be approved twice and every yes is correct in isolation. Nothing in the approval layer relates one to another. The harness authorizes a tool call; the application cares about an operation. Different units. So the memory lives where the consequence lands. The payer mints the operation id server-side and fingerprints the exact payload at prepare time, then re-verifies that fingerprint at commit. A retry of the identical payload replays the original receipt instead of writing again. A tampered amount is refused before anything is written. That check does not depend on the model behaving well. In the demo I approve twice and the ledger holds one row, same receipt. Then I ask the agent to change the amount: it refuses on its own, and the end-to-end suite shows the payer would refuse it anyway. Three operations attempted, exactly one row written. Idempotency is not new. What is new is that the thing retrying is an agent and the thing approving is a person. Relating one approval to another is an application-side concern, confirmed as such by TrueFoundry before this was recorded. Uses MCP over Streamable HTTP, Code Mode sandbox, shielded tools, and one skill (safe-resubmission) carrying the retry policy. All data synthetic; the ledger is local SQLite with immutable rows.
TrueFoundryMCP (Streamable HTTP), Node.js, SQLite (better-sqlite3), OpenAIKeep exploring what builders shipped.
Sports Whisperer
SportsWhisperer
With AI becoming centre stage - Content and Entertainment will be the king. Sport has the highest amount of spend as industry and creates massive economic drive, so we have built an All in One - Sports Whisperer for all major sports Crickets, NFL, Soccer, Basketball, Baseball, etc where the novice and experienced players can interact with the favorite games and players !!! More details captured - https://docs.google.com/presentation/d/1_q0SDTvutQP9kd2SLdDXYYkc-h9QVYwj0vdih1Cuqzc/edit?slide=id.gcb9a0b074_1_0#slide=id.gcb9a0b074_1_0
AgentInvariant
AgentInvariant
AgentInvariant is a behavioral safety evaluator for tool-using AI agents. Agent workflows are probabilistic: two requests with the same meaning can produce materially different external actions. AgentInvariant uses metamorphic testing to determine whether critical operational behavior remains consistent across meaning-equivalent inputs. For each input variant, AgentInvariant starts a fresh OpenAI conversation and an isolated SQLite database. The target agent uses real tools to check coverage, retrieve authorization requirements, record business approval, and submit a synthetic prior-authorization request. AgentInvariant records the complete structured tool trace and evaluates it using deterministic Python invariants—not an LLM judge. It verifies that exactly one submission occurs and that matching coverage and business approval are successfully recorded before submission. The evaluator is exposed through a Streamable HTTP MCP server and invoked by a TrueForge agent using the `evaluate_agent` tool. Results include per-variant PASS or BLOCKED decisions, ordered traces, exact invariant violations, compliance rate, and the shortest failing counterexample. The demo compares an explicitly labeled unsafe negative control, which AgentInvariant correctly blocks, with a hardened candidate that follows the required workflow. All healthcare data is synthetic. This project is an administrative workflow reliability demonstration and is not clinical decision support.
HackerSquad project
Robo Harness
Hardess for robo