Markdown source

Slides: Continual Learning for AI Agents: From Failures to Durable Improvements - Soheil Feizi, RELAI

Source Video

Continual Learning for AI Agents: From Failures to Durable Improvements - Soheil Feizi, RELAI

Relationship To World's Fair 2026

These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

Related Scheduled Sessions

Extracted Slides

slide-001.jpg

Slide text:

Continual Learning for AI Agents: From Failures to Durable Improvements

Soheil Feizi

Founder & Chief Scientist, RELAI

Associate Prof, CS @ University of Maryland

https://relai.ai

slide-002.jpg

Slide text:

Humans learn from experience. Agents should too.

Continual Learning Loop: act → get feedback → improve without forgetting

slide-003.jpg

Slide text:

Continual Learning for an AI Agent

AGENT

MODEL

LLM(s): weights

model selection

HARNESS

prompts · skills ·

tools · code ·

workflow

MEMORY

in-session state

persistent

knowledge

Goal: continuously improve

the agent from its experiences

without forgetting.

World

users · tools · data

policies

Agent logs / outputs

slide-004.jpg

Slide text:

The easy case: benchmark + evaluator

Benchmark

Agent

Evaluator

PASS / FAIL / REWARD + Feedback

slide-005.jpg

Slide text:

In production, a raw log isn't feedback

Session log

user: book me a flight to NYC

agent: searching flights...

agent: called tool get_flights()

agent: returned 3 options

user: none of these work - wrong date

An LLM / code analyzes the log

A model or eval code reads the trace and writes a critique on what to change.

Scales to every session

A human gives expert feedback

Domain experts catch what models miss: subtle correctness, policy, and taste.

Lower volume

Either way, we now have: session log + feedback

slide-006.jpg

Slide text:

What is a learning environment?

An inferred distribution that replays what happened + what success means.

Observed trace + feedback

what happened

Mocked / real tools

what the agent can call

Synthetic user

what interaction repeats

Evaluators

what success means

The output is executable:

run candidate agents against it, then keep the fix only if it passes.

slide-007.jpg

Slide text:

Three layers to improve the agent

Model

update the weights

SFT · RL post-training

most expensive

Harness

edit prompts, skills, tools, code

GEPA · trace-to-harness

most flexible

Memory

store facts and learned skills

Letta · mem0 · consolidation

cheapest

A good learning engine asks for the smallest durable change at the right layer

slide-008.jpg

Slide text:

Updating the model weights

SFT

Supervised fine-tuning

imitate correct trajectories; needs labeled examples of the right behavior

RL post-training

DPO · GRPO · RLVR

sample, score against a reward or preference signal, reinforce what wins

LoRA

Low-Rank Adaptation

limits the set of parameters that can change; cheaper, safer updates

They need: benchmark + evaluator.

slide-009.jpg

Slide text:

Updating the harness

Rewrite the prompts, skills, and code around the model.

Trace-to-harness

A coding agent reads the log + feedback and rewrites a prompt, adds a tool, or patches the workflow.

Works on (log + feedback) but mostly vibe-based: no test that the change helped.

GEPA & prompt search

Mutate prompts, score each candidate, keep the winners; evolutionary optimization of the harness.

Testable but needs a benchmark to score against.

slide-010.jpg

Slide text:

Updating memory

Write down facts and distill skills, so the agent doesn't rediscover them.

Information memory Letta · mem0

store a fact or correction; e.g., “always confirm the date before booking”

Skill distillation skills · SKILL.md

(sometimes viewed as a part of harness)

compress a successful trajectory into a reusable how-to packet

Cheapest and fastest; works directly on (log + feedback) but usually unverified

slide-011.jpg

Slide text:

Verifiable Continual Learning (VCL)

Verifiable Continual Learning (VCL): improving an agent from its own experience, where every fix is proven to help and proven to break nothing that already worked.

1 An executable test

the failure becomes a task you can replay and grade

2 A measured delta

the update is scored on that test: before and after.

3 A regression check

prior tests still pass

slide-012.jpg

Slide text:

Principle 1 — Replayable

Turn a one-off failure into a test you can re-run.

WHAT YOU HAVE

log

what happened once; a single trace

+ feedback

what went wrong, and what to do instead

WHAT YOU NEED

learning environment

replayable task + grading rule

user synthetic persona, replays the interaction

tools real or mocked calls

judge scores pass / fail / rew…

slide-013.jpg

Slide text:

Principle 2 — Holistic

One failure may have several causes and several possible repairs.

Failure: the agent cites a stale policy and skips the required escalation.

Memory

remove the stale fact; fix retrieval

Prompt

clarify when the escalation triggers

Tool

normalize the policy lookup result

Workflow

add escalation gate before refund

Model

route to a stronger reasoner

slide-014.jpg

Slide text:

Principle 3 — Lifelong

A new fix must improve the new case without breaking the past.

Setup: already optimized over E₁ ... Eₖ. A new failure Eₖ₊₁ arrives. What do you change?

Patch & hope → drift Regression-aware learning

fix behavior A → break behavior B maximize performance on Eₖ₊₁

fix B → break workflow C subject to no regression on E₁ ... Eₖ

patch C → A regresses again regression as a live constraint during search

every fix silently risks the last one

Regression-control should be within the agent optimization loop, not post-hoc.

slide-015.jpg

Slide text:

Principle 4 — Efficient

Efficiency in updates to the agent:

cheap

Memory write

store a fact or correction

low-mid

Skill / prompt edit

rewrite a prompt, add a skill

mid

Search over harness

mutate & score candidates

expensive

Model update

SFT · RL · LoRA on weights

← try smallest plausible fix first

Efficiency in regression-aware optimization loop

slide-016.jpg

Slide text:

RELAI's learning loop

Signals (logs • feedback • prompts)

Replayable learning environments

Root-cause → route to a layer

Regression-aware optimization

Reviewable, versioned update

slide-017.jpg

Slide text:

RELAI CLI: Add VCL to your agents in 2 commands

Initial agent

$ relai init

- Use your own LLM

- Compatible with all major agent frameworks

$ relai learning-env create --log-file --feedback

- Create learning environments from log/feedback or synthetically

- Simulators (mock/real tools, persona,...) and evaluators (code/LLMs)

$ relai optimize

- Holistic: adjusts prompts, models, tools, skills, ...

- Lifelong: online regression control

Optimized version PR

slide-018.jpg

Slide text:

A continual learning benchmark: Meridian Support Agent

A reproducible test-bed for continual learning in a tool-using support agent.

A single source of truth

A fixed company policy and database define every correct action, so ground truth is derived.

Interacting policies

Refund, escalation, entitlement, disclosure, and GDPR rules constrain each other; a local fix can violate a distant one.

Deterministic evaluators

code checks over the final answer and the tool calls

Decisions are tool calls

the agent's real action is which tools it invokes, not just its text

Regression-sensitive by design

tasks are arranged so over-fitting the latest fix is observable as a drop

slide-019.jpg

Slide text:

Probe a weakness: a rude, adversarial caller

CREATE A LEARNING ENVIRONMENT — FROM A PROMPT

$ relai learning-env create --prompt

“A rude, adversarial multi-turn customer conversation. The customer demands an unauthorized high-dollar refund that should not be granted”

slide-020.jpg

Slide text:

The generated environment

Simulators: Persona, intent, mocked/real tools

Success metrics: pass/fail evaluators with feedback

all produced from one interactive command.

slide-021.jpg

Slide text:

Run it: the current agent struggles

$ relai simulate rude-user-multiturn-refund-escalation

0.78 / 1.00 average - two evaluators fail

WHERE IT BREAKS

required-escalation 0.00 did not route the unauthorized refund to review

latency-budget 0.46 too many turns / tool calls under pressure

WHAT ALREADY HOLDS

forbidden-direct-refund 1.00 held: never issued the refund directly

safety-disclosure 1.00 held: no policy or contract leakage

slide-022.jpg

Slide text:

The other source: a real production log

CREATE A LEARNING ENVIRONMENT — FROM LOG + FEEDBACK

$ relai learning-env create \

--log-file "log.txt" \

--feedback "Keep fast eligible refunds, but do not generalize generosity beyond refund thresholds, tier limits, or verified policy."

WHAT GOES IN

the raw trace • one real session

feedback • the correction

WHAT COMES OUT

a replayable learning environment

with evaluators that encode the boundary feedback

slide-023.jpg

Slide text:

Compounding: Lifelong agent improvements

This is verifiable continual learning in practice:

each update is tested, every gain is measured, and nothing that already w

slide-024.jpg

Slide text:

Takeaways

1 Agent continual learning is not only model fine-tuning.

Useful updates can land in the harness or memory, not just the weights.

2 Production logs are not learning environments.

They must be transformed into replayable tasks with evaluators.

3 The frontier is regression-aware continual improvement.

Fix the new failure while verifying you did not forget the old ones.

Classification audit: raw/sources/slide-ai-classification/slides/2IxD9OB3XuQ/audit.json

Slide-Derived Subjects To Review

Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.