AI Agent Rescue

89% of agent pilots never reach production. We fix the ones worth saving.

Most agent projects do not fail in the model — they fail in system design, error handling, and governance. AI Agent Rescue is a diagnostic-first engagement: we instrument your agent, find the real failure modes with runtime evidence, and re-architect for resilience so it can finally cross into production.

AI Agent Rescue is a remediation engagement for AI agents that stall between pilot and production. We instrument the agent with tracing, reproduce the failures, and produce a root-cause report covering tool errors, memory/state issues, unhandled edge cases, and context debt — then harden the architecture with retries, state recovery, human-in-the-loop gates, and an evaluation harness. A diagnostic runs 1–3 weeks, followed by fixed-scope remediation.

Why Now

The failure numbers are stark: by 2026 roughly 89% of enterprise agent pilots never reach production, and 35–45% of those that do are deprecated within a year. Crucially, the dominant failure modes are not hallucination — they are tool errors (~28%), memory and state issues (~22%), and unhandled edge cases (~18%). Gartner expects more than 40% of agentic AI projects to be cancelled by end of 2027, most often due to "context debt" and missing observability rather than model limitations. These are engineering problems, and engineering problems are fixable.

89%

Enterprise AI agent pilots that never reach production

Gartner / Deloitte, 2026

~28%

Share of agent incidents caused by tool errors — the #1 failure mode, not hallucination

AI Agent Failure-Mode Statistics, 2026

>40%

Agentic AI projects expected to be cancelled by end of 2027

Gartner, 2025

What You Get

Instrumentation and tracing of the existing agent

Reproduction of the failure modes with runtime evidence

Root-cause report: tool errors, state/memory, edge cases, context debt

Evaluation harness with production-trace replay

Architecture hardening: retries/backoff, state recovery, guardrails

Human-in-the-loop approval gates for high-stakes steps

Observability dashboards (OpenTelemetry) for ongoing monitoring

Stabilization plan with clear go / no-go gates

How It Works

Instrument & Reproduce

We add distributed tracing and reproduce the failures instead of guessing — no fix without runtime evidence.

Root-Cause Analysis

We pinpoint whether failures come from tool calls, memory/state, edge cases, or context debt, and quantify each.

Stabilize & Harden

We add error-recovery logic, state management, guardrails, and human-in-the-loop gates at the points that actually break.

Eval Harness & Handover

A regression-catching evaluation suite plus dashboards and documentation so the agent stays healthy in production.

Who It's For

Teams with a pilot that demos well but stalls in production
Agents with silent regressions or unexplained wrong answers
"Archaeology projects" nobody can debug without traceability
Deployments losing stakeholder trust before cancellation

Frameworks & Tools

OpenTelemetryLangSmithLangfuseArizeLangGraphTemporal

Timeline1–3 week diagnostic, then fixed-scope remediation

PricingDiagnostic scoped to agent complexity

What This Delivers

Representative outcomes based on typical engagements and industry benchmarks.

1–3 wks

Diagnostic to a runtime-evidence root-cause report

Top 3

Failure modes targeted: tool errors, state/memory, edge cases

100%

Fixes backed by traces and evals — not guesses

See full results

Frequently Asked Questions

Rarely because of the model. In 2026 the dominant failure modes are tool execution errors (~28%), memory and state issues (~22%), and unhandled edge cases (~18%). The common root cause is "context debt" — the gap between what the agent assumes your data means and what your business actually means — compounded by missing observability.

We start diagnostic-first and salvage as much of your existing agent as makes sense. Most rescues are re-architecture around resilience and evaluation, not a full rewrite — though we will tell you honestly if a rebuild is cheaper.

The diagnostic phase runs 1–3 weeks: we instrument the agent, reproduce the failures, and deliver a root-cause report. Remediation is then scoped as fixed-price work against that report.

An evaluation harness with production-trace replay and observability dashboards, so regressions are caught automatically instead of surfacing as broken stakeholder-facing output weeks later.

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Ready to start your AI Agent Rescue?

Typical timeline: 1–3 week diagnostic, then fixed-scope remediation. Tell us about your situation and we'll scope it in a free call.

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