he hard-won lessons learned in developing its offering -- and the fact that many
of these lessons sound a lot like what we find in classic management texts:

LESSON 1: DELEGATION MATTERS.

When Anthropic analyzed what factors lead to excellent performance by Claude
Research, it turned out that the best agentic systems weren't necessarily built
on the best or most expensive AI models. Rather, like a good human manager, they
need to excel at breaking down and distributing tasks to their digital workers.

Unlike human teams, agentic systems can enlist as many AI workers as needed,
onboard them instantly and immediately set them to work. Organizations that can
exploit this scalability property of AI will gain a key advantage, but the hard
part is assigning each of them to contribute meaningful, complementary work to
the overall project.

In classical management, this is called delegation. Any good manager knows that,
even if they have the most experience and the strongest skills of anyone on
their team, they can't do it all alone. Delegation is necessary to harness the
collective capacity of their team. It turns out this is crucial to AI, too.

The authors explain this result in terms of 'parallelization': Being able to
separate the work into small chunks allows many AI agents to contribute work
simultaneously, each focusing on one piece of the problem. The research report
attributes 80 per cent of the performance differences between agentic AI systems
to the total amount of computing resources they leverage.

Whether or not each individual agent is the smartest in the digital toolbox, the
collective has more capacity for reasoning when there are many AI 'hands'
working together. In addition to the quality of the output, teams working in
parallel get work done faster. Anthropic says that reconfiguring its AI agents
to work in parallel improved research speed by 90 per cent.

Anthropic's report on how to orchestrate agentic systems effectively reads like
a classical delegation training manual: Provide a clear objective, specify the
output you expect and provide guidance on what tools to use, and set boundaries.
When the objective and output format is not clear, workers may come back with
irrelevant or irreconcilable information.

LESSON 2: ITERATION MATTERS.

Edison famously tested thousands of light bulb designs and filament materials
before arriving at a workable solution. Likewise, successful agentic AI systems
work far better when they are allowed to learn from their early attempts and
then try again. Claude Research spawns a multitude of AI agents, each doubling
and tripling back on their own work as they go through a trial-and-error process
to land on the right results.

This is exactly how management researchers have recommended organizations staff
novel projects where large teams are tasked with exploring unfamiliar terrain:
Teams should split up and conduct trial-and-error learning, in parallel, like a
pharmaceutical company progressing multiple molecules towards a potential
clinical trial. Even when one candidate seems to have the strongest chances at
the outset, there is no telling in advance which one will improve the most as it
is iterated upon.

The advantage of using AI for this iterative process is speed: AI agents can
complete and retry their tasks in milliseconds. A recent report from Microsoft
Research illustrates this. Its agentic AI system launched up to five AI worker
teams in a race to finish a task first, each plotting and pursuing its own
iterative path to the destination. They found that a five-team system typically
returned results about twice as fast as a single AI worker team with no loss in
effectiveness, although at the cost of about twice as much total computing
spend.

Going further, Claude Research's system design endowed its top-level AI agent --
the 'Lead Researcher' -- with the decision authority to delegate more research
iterations if it was not satisfied with the results returned by its sub-agents.
They managed the choice of whether or not they should continue their iterative
search loop, to a limit. To the extent that agentic AI mirrors the world of
human management, this might be one of the most important topics to watch going
forward. Deciding when to stop and what is 'good enough' has always been one of
the hardest problems organizations face.

LESSON 3: EFFECTIVE INFORMATION SHARING MATTERS.

If you work in a manufacturing department, you wouldn't rely on your division
chief to explain the specs you need to meet for a new product. You would go
straight to the source: the domain experts in R&D. Successful organizations need
to be able to share complex information efficiently both vertically and
horizontally.

To solve the horizontal sharing problem for Claude Research, Anthropic innovated
a novel mechanism for AI agents to share their outputs directly with each other
by writing directly to a common file system, like a corporate intranet. In
addition to saving on the cost of the central coordinator having to consume
every sub-agent's output, this approach helps resolve the information
bottleneck. It enables AI agents that have become specialized in their tasks to
own how their content is presented to the larger digital team. This is a smart
way to leverage the superhuman scope of AI workers, enabling each of many AI
agents to act as distinct subject matter experts.

In effect, Anthropic's AI Lead Researchers must be generalist managers. Their
job is to see the big picture and translate that into the guidance that
sub-agents need to do their work. They don't need to be experts on every task
the sub-agents are performing. The parallel goes further: AIs working together
also need to know the limits of information sharing, like what kinds of tasks
don't make sense to distribute horizontally.

Management scholars suggest that human organizations focus on automating the
smallest tasks; the ones that are most repeatable and that can be executed the
most independently. Tasks that require more interaction between people tend to
go slower, since the communication not only adds overhead, but is something that
many struggle to do effectively.

Anthropic found much the same was true of its AI agents: "Domains that require
all agents to share the same context or involve many dependencies between agents
are not a good fit for multi-agent systems today." This is why the company
focused its premier agentic AI feature on research, a process that can leverage
a large number of sub-agents each performing repetitive, isolated searches
before compiling and synthesizing the results.

All of these lessons lead to the conclusion that knowing your team and paying
keen attention to how to get the best out of them will continue to be the most
important skill of successful managers of both humans and AIs. With humans, we
call this leadership skill empathy. That concept doesn't apply to AIs, but the
techniques of empathic managers do.

Anthropic got the most out of its AI agents by performing a thoughtful,
systematic analysis of their performance and what supports they benefited from,
and then used that insight to optimize ho
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