Parallel investigation

Most research work is independent. The fundamental analyst does not need the technical analyst’s output to do its job. Run them at the same time. A Parallel block in a Workflow does exactly that: every step inside it runs concurrently, and the pipeline waits for all of them before moving on.

from agno.workflow import Parallel, Step, Workflow

workflow = Workflow(
    id="review",
    name="Deep Dive Review",
    steps=[
        Step(name="Market Assessment", agent=market_analyst),
        Parallel(
            Step(name="Fundamental Analysis", agent=financial_analyst),
            Step(name="Technical Analysis", agent=technical_analyst),
            name="Deep Dive",
        ),
        Step(name="Risk Assessment", agent=risk_officer),
    ],
)

result = workflow.run("Run a full review on NVDA")
final = result.content  # the last step's output

# Gotcha: result.step_results is positional, and a Parallel block is one
# StepOutput whose .steps holds the sub-steps, not flattened:
#   [ StepOutput("Market Assessment"),
#     StepOutput("Deep Dive", steps=[StepOutput("Fundamental Analysis"),
#                                    StepOutput("Technical Analysis")]),
#     StepOutput("Risk Assessment") ]

The market assessment runs first because risk and the deep dive depend on it. Fundamental and technical analysis have no dependency on each other, so they run together. Risk waits for both.

Two ways to fan out

Shape	Use when
`Parallel` step in a Workflow	The investigations and their position in the step graph are fixed
Broadcast Team	A lead should synthesize independent opinions on one question

A Broadcast team is the adaptive version: every member evaluates the same question simultaneously and the lead reconciles them. See Orchestration patterns.

Sequence only what depends

The skill is dependency analysis, not maximum parallelism. Put a step in Parallel only when it does not read another step’s output. Keep the genuine dependencies sequential.

Step	Depends on	Runs
Market assessment	Nothing	First, alone
Fundamental analysis	Market context	In parallel with technical
Technical analysis	Market context	In parallel with fundamental
Risk assessment	Both analyses	After the parallel block

Why this matters for deep research

Deep research is slow because it is thorough. Parallelism keeps thoroughness affordable in wall-clock time: the two analyses above finish in roughly the time of the slower one rather than the sum of both.

Next steps

Task	Guide
Choose the orchestration shape	Orchestration patterns
Ground each parallel specialist	Grounding research
Synthesize into one artifact	Structured deliverable

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Parallel investigation

Two ways to fan out

Sequence only what depends

Why this matters for deep research

Next steps

Developer Resources

​Two ways to fan out

​Sequence only what depends

​Why this matters for deep research

​Next steps

​Developer Resources

Two ways to fan out

Sequence only what depends

Why this matters for deep research

Next steps

Developer Resources