Exception Topology Mapper
by Al1as
Map the geometry of system failures to transform edge-case chaos into structured architectural governance.
New: UPI payments are live. Buyers in India can now pay for skills with UPI in INR -> Browse skills
THE AGENSI STORE
48 skills found
by Al1as
Map the geometry of system failures to transform edge-case chaos into structured architectural governance.
by SkillForge
Architect production-grade multi-agent systems with explicit contracts, idempotency, and self-healing reliability.
by SkillForge
Stop fragile agent chains with structured, versioned, and idempotent handoff contracts for multi-agent systems.
by Timoranjes
Teaches AI coding agents to systematically verify their own work before declaring "done." Implements a structured five-check verification gauntlet (build/test/lint/git-diff/runtime) that the agent run
by John Barros
Architect resilient, human-in-the-loop agent workflows with strict stop criteria, budgets, and validation rubrics.
by Timoranjes
Teaches AI coding agents to build and run automated regression tests for SKILL.md files. When you update a skill that your team depends on, you need to know it still works — not just that it "looks ri
Map your real cloud dependency tree — data plane and control plane — find the single points of failure ranked by blast radius, catch the classic traps like monitoring that dies with the region it watches, and get a prioritized resilience backlog. The audit every outage post-mortem says to do first.
by Shippers
A canonical eight-layer architectural stack and enforcement framework for building reliable autonomous agents.
by Timoranjes
Teaches AI coding agents to self-detect context rot (regression loops, instruction drift, hallucination drift, lost-in-the-middle) during long sessions and execute a structured checkpoint/recovery pro
by Timoranjes
Teaches AI coding agents to perform structured, high-signal code reviews specifically for AI-generated code — catching the failure modes unique to LLM output (confident hallucinations, silent error sw
by Shogun Labs
Battle-tested prompting patterns to eliminate LLM output drift. Sandwich structure, few-shot examples, history limits, retry, and token caps — 6 composable layers for production-grade agent reliability.
by Timoranjes
Teaches AI coding agents to handle API and tool call failures gracefully using production-grade resilience patterns: exponential backoff with jitter, circuit breakers, timeout management, rate-limit h
by Shogun Labs
Battle-tested prompting patterns to eliminate LLM output drift. Sandwich structure, few-shot examples, history limits, retry, and token caps — 6 composable layers for production-grade agent reliability.
by heyhridyansh
Production-grade reliability auditing for background jobs, queue consumers, and asynchronous workflows.
by heyhridyansh
Audits AI agents and automated workflows for infinite loops, cost spikes, and runaway execution risks.
by Edric Vale
Coordinate multi-step detached jobs as one durable TaskFlow run: define an owner context and state machine with ordered steps, retries, webhook waits, and resum
by heyhridyansh
Production-grade reliability and security audit for inbound and outbound webhook integrations.
by Vivek K
Designs multi-system enterprise automation the right way: what to automate vs keep manual, build vs buy vs orchestrate, idempotency and HITL gates, security, SLA/SLOs, safe rollout, and ROI — without running the workflow.
by monna
Trace multi-agent failures, separate roots from symptoms, and design the smallest evidence-producing rerun.
Stress-test AI agents for security, reliability, tool abuse, data leakage, failure modes, and production readiness before deployment.
Design and harden AI workflows against failures, duplicate actions, retries, rate limits, tool outages, and unreliable agent behavior.
AI Agent Reliability Architect is a production-grade reliability engineering system for AI agents. It systematically discovers capabilities, maps failure modes, generates golden-path, boundary, recovery, and adversarial tests, evaluates tool use and state behavior, and produces an evidence-based Agent Reliability Score™. It includes a deliberate human review checkpoint, remediation planning, regression testing, and final QA—helping teams identify hidden failures and harden AI agents before deployment.