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AI Failures and Smart Evaluation Techniques: 2026 Updated Replay

Watch the Future AGI webinar on AI evaluation, updated for 2026. Covers why classic test suites miss agent failures and a live evals walkthrough.

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Updated
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3 min read
evaluations webinars
Webinar 01: AI Failures and Smart Evaluation Techniques
Table of Contents

Watch the Webinar Replay (2026 Update)

TL;DR: AI Failures and Smart Evaluation in 2026

TopicWhat you walk away with
Why classic ML metrics failGenerative outputs have many valid answers, so accuracy and F1 miss faithfulness, groundedness, and tool-use bugs.
Real-world failure case studiesAir Canada 2024 refund hallucination, Microsoft Tay 2016 input poisoning, NYC MyCity 2024 illegal-advice incident.
Smart evaluation stackFour-layer evals: input checks, model output, retrieval grounding, agent trajectory.
Live demo toolingfi.evals.evaluate with turing_flash (about 1 to 2 seconds), turing_small (about 2 to 3 seconds), and turing_large (about 3 to 5 seconds) cloud judges.
Runtime defenseAgent Command Center at /platform/monitor/command-center for routing, budget caps, and inline guardrails.
Who benefits mostML engineers, AI applied scientists, trust and safety leads, and platform teams shipping LLM features.

AI agents are rapidly transforming industries powering chatbots, automating workflows, and making real-time decisions in finance, healthcare, and cybersecurity. As these agents become more autonomous, the question is no longer “does the model speak fluently” but “did the agent stay grounded, follow instructions, and respect policy on this specific run?”

Key Takeaways

  1. The Growing Importance of AI Evaluation Metrics. Why a single number cannot capture multi-objective LLM quality, and how the four-layer eval stack (input, output, retrieval, trajectory) maps onto real product surfaces.
  2. High-Profile AI Failures and Their Lessons. Air Canada’s 2024 refund-policy hallucination (CBC News), Microsoft Tay’s 2016 input poisoning (The Verge), and the 2024 NYC MyCity chatbot incident (The Markup). Each shows a specific eval gap that ai-evaluation now closes.
  3. Smart Evaluation Strategies. Mixing deterministic checks (regex, JSON schema, exact match) with LLM-as-judge calls using fi.evals.metrics.CustomLLMJudge and fi.evals.llm.LiteLLMProvider, calibrated against a small human-labelled gold set.
  4. The Future of AI Evaluation. Online evaluation, sampling strategies, drift detection, and the move from “did the model pass the test set” to “is this specific production trace acceptable right now.”
  5. Hands-on Demo with Future AGI. A live walk through from fi.evals import evaluate, Evaluator, plus fi_instrumentation.register and FITracer to wire traces into the dashboard, then the Agent Command Center to block unsafe outputs at the gateway.

The Live Demo Stack in 2026

from fi.evals import evaluate, Evaluator
from fi.evals.metrics import CustomLLMJudge
from fi.evals.llm import LiteLLMProvider
from fi_instrumentation import register, FITracer

# 1. Register the traceAI tracer once at process boot
tracer_provider = register(
    project_name="ai-failures-webinar-2026",
    project_version_name="v1",
)
tracer = FITracer(tracer_provider)

# 2. Run a faithfulness check from the cloud catalog
result = evaluate(
    "faithfulness",
    output="The Air Canada bereavement fare is retroactive.",
    context="Air Canada bereavement fare policy is not retroactive.",
    model="turing_flash",
)
print(result.score, result.reason)

# 3. Or define a domain-specific judge once and reuse it
custom_judge = CustomLLMJudge(
    name="policy_grounding",
    rubric="Return 1 if the answer is supported by the policy excerpt, else 0.",
    provider=LiteLLMProvider(model="gpt-5-2025-08-07"),
)

The cloud judges run at roughly 1 to 2 seconds for turing_flash, 2 to 3 seconds for turing_small, and 3 to 5 seconds for turing_large, per the cloud-evals reference. Authentication uses FI_API_KEY and FI_SECRET_KEY environment variables, set once at deploy time.

For production guardrails, the same evaluators can run inline at the Agent Command Center, which acts as a control layer for supported model providers and self-hosted endpoints and adds routing, budget caps, semantic caching, and prompt-injection containment aligned with the OWASP LLM Top 10.

Who Should Watch the Replay

  • ML engineers and applied scientists building or maintaining LLM and agent features.
  • Trust and safety leads owning hallucination, toxicity, and prompt injection containment.
  • Platform and DevOps teams standing up observability and gating for AI workloads.
  • Product managers mapping eval metrics to user-facing quality targets.

Pair the replay with the agent evaluation frameworks roundup and the LLM evaluation architecture deep dive for a complete picture of the 2026 evaluation stack.

Further Reading and Primary Sources

If you have questions or want to walk through how Future AGI fits your stack, drop a note to the team at futureagi.com. The goal of the session is concrete: leave with a four-layer eval blueprint, three failure case studies you can cite in your own roadmap doc, and a starter snippet you can extend into a CI job (adding your own dependency pinning, threshold assertions, and non-zero exit codes for regressions).

Frequently asked questions

What does this webinar cover?
The session explains why classic ML test suites miss generative-AI failure modes, walks through three high-profile incidents (Air Canada chatbot, Microsoft Tay, NYC MyCity chatbot), and demos how Future AGI runs faithfulness, groundedness, and toxicity evaluations on LLM and agent traces. The replay closes with a live walkthrough of `fi.evals.evaluate` running cloud judges like `turing_flash` and `turing_large` in 1 to 5 seconds per row.
Who should watch the AI failures and smart evaluation webinar?
ML engineers, applied scientists, and product teams shipping LLM features in production. The webinar assumes basic familiarity with prompt engineering and RAG, then layers in evaluation strategy, regression testing, and online observability. Security, legal, and trust and safety leads also benefit from the section on hallucination root cause analysis and prompt injection containment patterns updated for 2026.
Why are traditional accuracy metrics insufficient for LLMs in 2026?
Accuracy assumes a single correct answer per input, which rarely exists in generative tasks. By 2026 most production failures show up as faithfulness drift, retrieval errors, tool-use loops, or unsafe outputs that pass simple exact match checks. The webinar covers the four-layer LLM evaluation stack that replaces single-metric scoring: input quality, model output, retrieval grounding, and agent trajectory.
Which evaluation metrics does the webinar demonstrate?
Faithfulness, groundedness, toxicity, prompt injection detection, instruction following, and task completion. Each runs as a string template through `fi.evals.evaluate` with cloud judges. The demo shows how to combine deterministic checks (regex, JSON schema) with LLM-as-judge calls so teams catch both hard bugs and quality regressions in the same pipeline.
How does Future AGI fit into an AI evaluation workflow?
Future AGI provides ai-evaluation (Apache 2.0), traceAI (Apache 2.0) for tracing across OpenAI, LangChain, LlamaIndex, MCP, and others, and a hosted platform for dashboards and Agent Command Center routing. Teams instrument code with `fi_instrumentation.register` and `FITracer`, then run online and offline evals using the same metric definitions. Self-hosted, hybrid, and cloud deployment options are available so each team can pick the data-handling model that matches their policy.
What real incidents are covered in the AI failures section?
Air Canada's 2024 chatbot ruling that bound the airline to a hallucinated refund policy, Microsoft's 2016 Tay disaster that became the textbook case for input red-teaming, and the 2024 NYC MyCity chatbot that advised business owners to break the law. Each is mapped to a specific evaluation gap that modern LLM eval frameworks now close.
Is the replay free and what tools are demoed?
Yes, the replay is free with email gating after the preview. The live segment shows ai-evaluation (Apache 2.0) imports for `from fi.evals import evaluate, Evaluator`, traceAI manual instrumentation, and the Agent Command Center at `/platform/monitor/command-center` for runtime guardrails. Teams can reproduce the walkthrough using their own `FI_API_KEY` and `FI_SECRET_KEY`.
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