Enhancing Agent Planning: Insights from LangChain

According
to
a
recent

LangChain
Blog
post,
planning
for
agents
remains
a
critical
challenge
for
developers
working
with
large
language
models
(LLMs).
The
article
delves
into
the
intricacies
of
planning
and
reasoning,
current
fixes,
and
future
expectations
for
agent
planning.

What
Exactly
Is
Meant
by
Planning
and
Reasoning?

Planning
and
reasoning
by
an
agent
involve
the
LLM’s
ability
to
decide
on
a
series
of
actions
based
on
available
information.
This
includes
both
short-term
and
long-term
steps.
The
LLM
evaluates
all
available
data
and
decides
on
the
first
step
it
should
take
immediately,
followed
by
subsequent
actions.

Most
developers
use
function
calling
to
enable
LLMs
to
choose
actions.
Function
calling,
first
introduced
by
OpenAI
in
June
2023,
allows
developers
to
provide
JSON
schemas
for
different
functions,
enabling
the
LLM
to
match
its
output
with
these
schemas.
While
function
calling
helps
in
immediate
actions,
long-term
planning
remains
a
significant
challenge
due
to
the
need
for
the
LLM
to
think
about
a
longer
time
horizon
while
managing
short-term
actions.

Current
Fixes
to
Improve
Planning
by
Agents

One
of
the
simplest
fixes
is
ensuring
the
LLM
has
all
the
necessary
information
to
reason
and
plan
appropriately.
Often,
the
prompt
passed
into
the
LLM
lacks
sufficient
information
for
reasonable
decision-making.
Adding
a
retrieval
step
or
clarifying
prompt
instructions
can
significantly
improve
outcomes.

Another
recommendation
is
changing
the
cognitive
architecture
of
the
application.
Cognitive
architectures
can
be
categorized
into
general-purpose
and
domain-specific
architectures.
General-purpose
architectures,
like
the
“plan
and
solve”
and
Reflexion
architectures,
provide
a
generic
approach
to
better
reasoning.
However,
these
may
be
too
general
for
practical
use,
leading
to
the
preference
for
domain-specific
cognitive
architectures.

General
Purpose
vs.
Domain
Specific
Cognitive
Architectures

General-purpose
cognitive
architectures
aim
to
improve
reasoning
generically
and
can
be
applied
to
any
task.
For
example,
the
“plan
and
solve”
architecture
involves
planning
first
and
then
executing
each
step.
The
Reflexion
architecture
includes
a
reflection
step
after
task
completion
to
evaluate
correctness.

Domain-specific
cognitive
architectures,
on
the
other
hand,
are
tailored
to
specific
tasks.
These
often
include
domain-specific
classification,
routing,
and
verification
steps.
The
AlphaCodium
paper
demonstrates
this
with
a
flow
engineering
approach,
specifying
steps
like
coming
up
with
tests,
then
a
solution,
and
iterating
on
more
tests.
This
method
is
highly
specific
to
the
problem
at
hand
and
may
not
be
applicable
to
other
tasks.

Why
Are
Domain
Specific
Cognitive
Architectures
So
Helpful?

Domain-specific
cognitive
architectures
help
by
providing
explicit
instructions,
either
through
prompt
instructions
or
hardcoded
transitions
in
code.
This
method
effectively
removes
some
planning
responsibilities
from
the
LLM,
allowing
engineers
to
handle
the
planning
aspect.
For
instance,
in
the
AlphaCodium
example,
the
steps
are
predefined,
guiding
the
LLM
through
the
process.

Nearly
all
advanced
agents
in
production
utilize
highly
domain-specific
and
custom
cognitive
architectures.
LangChain
makes
building
these
custom
architectures
easier
with
LangGraph,
designed
for
high
controllability,
which
is
essential
for
creating
reliable
custom
cognitive
architectures.

The
Future
of
Planning
and
Reasoning

The
LLM
space
has
been
evolving
rapidly,
and
this
trend
is
expected
to
continue.
General-purpose
reasoning
is
likely
to
become
more
integrated
into
the
model
layer,
making
models
more
intelligent
and
capable
of
handling
larger
contexts.
However,
there
will
always
be
a
need
to
communicate
specific
instructions
to
the
agent,
whether
through
prompting
or
custom
cognitive
architectures.

LangChain
remains
optimistic
about
the
future
of
LangGraph,
believing
that
as
LLMs
improve,
the
need
for
custom
architectures
will
persist,
especially
for
task-specific
agents.
The
company
is
committed
to
enhancing
the
controllability
and
reliability
of
these
architectures.

Image
source:
Shutterstock