Conventional thinking is that agentic
AI
is
thrown
around
enough
to
be a cliché. We
aren’t sure
what
it
means. We
aren’t
sure
what
it
does. Whatever
it
is
and
does
really
doesn’t
mean
much
for
legal,
right?
I’ve
heard
all
sorts
of tech concepts
thrown
around during the
first
two
days of CES:
robotics,
longevity,
vertical
AI,
industrial
AI.
But
the
one
talked
about
the
most
is
agentic
AI
and
what
it
can
do.
I
admit I’ve
been
a
skeptic. But
the
press conferences and
keynotes
by
the
big
players
like Nvidia, Samsung, LG, and
a
smaller player, AGI
Inc., suggest that
agentic
is
not
just
some
pie
in
the
sky hyperbole but
a
real
thing
that
can
do
real
things for real people.
As Amit
Jain,
CEO
of Luma AI, put
it as
a
guest
in the
AMT Keynote,
“2026
will
be
the
year
of
the
agents.”
I’m now convinced
that
agentic
AI
is
enough
of
a
thing
for
legal
to
begin
thinking
about
its
benefit
and
risks.
What Agentic AI
May
Mean
What
all these keynote speakers
talked
about
was
the potential
for agentic
AI
to
create
an
honest
to
God
assistant.
Not
a
dumb
GenAI
assistant
that
tries
to
answer just what
you
ask,
albeit
often unsuccessfully.
But
a
real
assistant
who
answers
you,
makes
suggestions
for
next
steps,
helps
you
think through solutions,
and
helps
you implement
them.
It’s
like having a devoted friend at
your
side
whose
only
job
is
to
help
in
every
way.
Some consumer examples mentioned
by CES
speakers demonstrate
what agentic AI may do for business. An agent
can
decipher
what’s
in
your fridge and create
a recipe
suggestion
based
on
what’s
there
and
what
you
have
historically
liked.
It
can
figure
out
how
to
season
the
food
based
on
your
taste.
It
can
help
you
plan
an
anniversary
night
out
with
your
spouse
based
on
likes
and
dislikes
it knows.
It
can
call
you
an
Uber
based
on your request
to
get
you
an
Uber.
And
then
provide
you
with
all
the
information
you
need to
catch
the
Uber.
No
more
wandering
around
airport
trying
to
figure
out
where
and how
to
catch
your
Uber.
It doesn’t
take
much
imagination
to
think
of examples that
may
aid business
and
even
legal.
I’m
sure
you’re
thinking,
as I
was,
it’s
the CES same
old,
same
old.
Wild
claims
backed
by
extravagant
productions
with heartwarming videos
about
how
AI is going
to
change
our
world.
But
this
felt
a
little
different
once
I
reflected
on
a
couple
of my
own recent experiences and after I
heard
Jensen
Huang,
founder
and
CEO
of Nvidia, speak.
Some Personal
Examples
I
got
a
glimpse
of what
agentic
AI
could
do when
I
recently
tried
OpenAI’s
web
browser, ChatGPT Atlas.
I
asked it to
help
me
pick
flights
to
Las
Vegas
for
CES.
It
accurately
sorted
through
the
options on
several
websites,
directed
me
to
the best
one, and
with
my
permission,
booked
the
flight on
that
site.
It
saved
me
a
bunch
of clicks, and
it worked pretty
seamlessly.
I’m
pretty
sure
with
some
more
use,
it
would
learn
I
only
want
direct
flights
and would like
an
aisle
seat. Admittedly,
there
have
been criticisms of Atlas,
but its
capabilities
I
witnessed
suggests
its
potential.
As
for
the
notion
that
this
is
all
pie
in
the
sky
and
will
never
happen,
the
same
has
been
said
for self-driving cars.
I bought one
a
few
years
ago
and
while
the self-driving features
at
that
time
were
okay,
they
weren’t
something
I
used
every
day
or
relied
on. But
the improvements since
then are
remarkable. They
are
now
so
good, I
use them every
day on just
about
every
trip.
And
as Jensen pointed
out in
his
keynote,
much
of
what
is
going
on
with
these vehicles is
a
form
of and
powered
by agentic
AI.
The
notion that
we may
be
on
the
cusp
of
something big was reiterated when
Jensen
explained
why this
may
be
the
case.
Jensen’s
Keynote
Jensen
explained
why
agentic
AI may
be ready
to become
mainstream. Here’s why:
• Computing
power
has
grown exponentially.
• That
growth
has
enabled
AI
programs
to understand
and
grasp
data
in
things
like
PDFs,
images,
and
audio
files.
• AI
programs
are
seeking solutions from
multiple
LLM
and
cloud
servers,
increasing
the
amount
of
data
from
which
to
cull
answers.
• AI
programs
can
now
simulate solutions
for situations
for
which
there
is
an
absence
of
data
by recognizing
patterns
from
similar
situations
and
understanding
outcomes.
What
this
means,
says
Jensen, is
it
can
make
recommendations
as
to
what
to
do
and
tell
you
why
it
has
come up
with
its conclusions. Agentic
AI
can
now
understand
things
like
sequences
of
events.
It can
reason
through
new
problems
that
may demonstrate similar sequences. It can
reason
what
will
likely
happen
next. It can
encounter
something
new
upon
which it
has not
been
trained
or recognizes and nevertheless determine
what to
do.
Moreover,
Jensen
described the
ability
for
programmers
to
easily
make
customized LLMs
for
specific
needs
of
a
certain
business and
then have
that
LLM
combine
with
more
generic
LLMs
with
greater training and
more
simulations
for
tailored
outputs.
Jensen
says
the
result
of
all
this is a
creative,
helpful
assistant
that
can
think through what
needs
to
be
done
based
on
what
you
have
done
in
the
past,
the requirements, goals, and
visions
of
your
business,
and similar outcomes across
the
ecosystem. It’s
an
agent
that
understands
and
can
interact
with
our
world.
As you
might
expect,
Jensen
was
long
on
optimism
but
a
little
short
on
what all has
to be
in
place
for
agentic
AI
to
work
as
promised. It
remains
to
be
seen
if
Jensen
and
others
are
right.
But
there
is
enough
evidence
at
the
show
for
me
to
think
that agentic
AI
is
real.
Just
how
real,
we
don’t
know
yet.
But
it’s
enough
of
a
possibility
for
legal
not
to
ignore.
For
Legal: A Blessing
It’s easy
to
write
all
this
off
when
it
comes
to
legal.
The conventional view
is
that no
lawyer
in
their
right
mind
would
let
a
bot unilaterally act
and
make
decisions
for
it.
Much
too
dangerous.
And
of
course,
agents
are
probably
not
going
to
happen
in
legal
anyway.
But
as
my experience with
Atlas
and
my
car demonstrate,
agentic
AI
being
an
erstwhile
companion
even
in
legal
may
not
be
that
far
off.
Such
a
legal
companion
could
also
be
a tremendous aide.
It
could
almost
be
like
a
practice
mentor,
always
ready
to
help.
Here’s
an
example
of
how
this
could
work.
When
I
was
a
young
lawyer,
I
was
given
a
case
to
practice.
I
sat down and
drew
up
a
set
of interrogatories. It
was
not
until
the
eve
of
trial
that
I learned
that
out
of
ignorance,
I left
out
a
set
of fundamental,
standard
interrogatory questions:
who
is
your
expert
and
what
are
they
going
to
say. Had
I
been
able
to merely
feed the initiating complaint
into
an
agentic
AI
tool,
it
could
have
run
with
it
and
created a
case
playbook
that
included
a set
of
comprehensive interrogatory questions.
No
more
digging
through
countless
files
to
see
what
others
in
the
firm
or
elsewhere
had
done
in similar situations.
No
more
worrying
if
I
missed
something
critical.
No
more
waking
up
in
the
middle
of
the
night
wondering
if
I
had
filed
something
in
time.
Less
stress,
better
results.
Of
course,
all
this
depends
on
the
agent
giving
the
why
certain
things
need
to
be
done
so
the
human
can
decide,
given
the
particular
facts
of
the
case, that
they’re appropriate.
More
importantly,
its
viability
as
a
tool depends
on a human being in
the
loop
reading,
understanding,
and
evaluating
what
the
agent is
suggesting.
It’s
the
human
in
the
loop
problem
that
creates
a potential
curse.
The
challenge
is how
to
avoid
it.
And
a
Potential
Curse
The
truth
is
it’s the
human
of
the
loop that
can screw
things
up. For
example, I
had
a
case
where
a
tragic weather-related
accident
injured and
killed
several
people. Clearly, they
had
nothing
to
do
with
what
happened
other
than
they
were
in
the
wrong
place
at
the
wrong
time. But
one
of
the
lawyers
for
another
defendant
in
the
case filed their
standard
set
of
pleadings
which
included
the
claim
that
the
victims
negligently
contributed
to
their
injuries
and
deaths.
It
was
wrong for
multiple
reasons. Suffice
it
to
say,
the
media
picked
it
up
and
the
client
was embarrassed and possibly prejudiced.
I
mention
that
here
because
it
is
precisely
this
human
in
the
loop
problem
that
poses
the danger for lawyer
use
of agentic
AI. It’s
the
temptation
or inability to critically think through a
problem
and
determine
if
what
the
agent
suggests
is
appropriate
given
the
situation. It’s the
same
human
in
the
loop
problem
we
have
with
cybersecurity:
you
can
warn
and
warn
to
not
click
on
unknown
links
but
sooner
or
later, a
human
in
the
loop will
ignore
the
warning
and
do
it
anyway.
I
fear
that
will
end
up
being
the
curse
of
things
like
agentic
AI.
It’s too
easy
and
tempting
to
overrely
on
it, particularly for
busy
or,
for
that
matter,
lazy
lawyers
who
don’t
take
the
time
to
treat
the agent’s roadmap
with
some
skepticism.
Not
to
mention
the
fact,
as
I
have
discussed
before,
GenAI
tools
and
LLMs
have
the
propensity
to
rot
our
brains
and
diminish the critical thinking
skill
necessary
to
be
discriminating.
The
fact
that
so
many
lawyers
are
getting
caught
citing
nonexistent
cases
proves
the
point,
The
Agentic
Future
But
for
those
who
do
and
can
think
critically,
I can see
the
advantages
of
agentic
AI.
It’s
like
my
car.
I
can
tell
it
where
I
need
to
end
up
and
it
maps
the
route
and
by
and
large
drives
itself
there.
It saves me
time
and
energy and,
in
many
instances,
makes
it
less
likely
for
a
mistake
to
occur.
But
I
don’t
go
to
sleep
while it’s doing
so
because
sometimes
it
will
decide
to
act
in
a
way
not appropriate for
the
circumstances at
hand.
That’s
when
my
skills
and experience come
into
play.
The
challenge
we
have
as
a profession is
to
make
sure
we
don’t
end
up
with
a
profession
where
those
using
the
agents
don’t
know
how
to drive. That
requires
attention
to
training
and
the
challenges.
It
also
requires
understanding
what
agentic
can
—
and
can’t
do.
One
thing
we
can’t
do
is
ignore
it.
Things
that
you
say
will
never
happen,
indeed
don’t
happen
until
they
do.
And
by
then,
preparing
is too
late.
Stephen
Embry
is
a
lawyer,
speaker,
blogger,
and
writer.
He
publishes TechLaw
Crossroads,
a
blog
devoted
to
the
examination
of
the
tension
between
technology,
the
law,
and
the
practice
of
law.