Pull-quote: “Speed of agents matters less than honesty of agents. Critic and referee are how you build honesty into the swarm.”
Why this matters
The first wave of multi-agent OSINT systems was a swarm: ten agents reading the same inputs and producing summaries, which were then averaged. The result was confident-sounding mediocrity. The agents reinforced each other’s biases. The aggregator could not tell whether the consensus was real or echo.
The second wave adds structure to the swarm. Specifically, two roles that are missing in the naive design:
- Critic — adversarial review. The critic’s job is to find the weakest link in the analysts’ reasoning and challenge it.
- Referee — adjudicates when analysts disagree. The referee’s job is to apply explicit decision criteria and produce a final answer with explicit reasoning.
This is not a UI improvement. It is a structural change in what the system is.
Aquil’s swarm
Aquil runs a structured OSINT swarm with four roles:
- Sourcers — discover and ingest open-source signals (news, public data, leaks, public records, satellite imagery sources where licensed)
- Analysts — produce hypotheses, summarize evidence, and propose causal explanations
- Critic — reviews analyst output for unsupported claims, missing evidence, plausible alternative explanations, and reasoning gaps
- Referee — adjudicates when the analysts and the critic disagree, with explicit criteria
The critic is structurally different from the analysts: it does not propose new claims. Its only function is to challenge existing ones. The referee is structurally different again: it does not propose or challenge. It decides, with explicit reasoning that goes into the audit trail.
Causal-graph synthesis
On top of the swarm, Aquil produces a causal graph of the assessed situation — events as nodes, hypothesized causal relationships as edges, with confidence weights. The graph is the team’s shared mental model. It is updateable, queryable, and exportable.
A causal graph is not just a visualization. It is a structured commitment to what we think is going on. New evidence updates the graph; missing evidence flags weak edges; alternative hypotheses are visible as competing edges.
Why this works
The naive swarm fails because mediocre answers can hide behind a chorus. The structured swarm makes the chorus disagree on purpose, and then makes a referee adjudicate. The agents’ weaknesses are surfaced rather than averaged. The team gets a more honest answer.
Closing
Speed of agents matters less than honesty of agents. The critic and the referee are how you build honesty into the swarm. Aquil is structured around that thesis.
