Reference · Section 21

The typical Adversarial Agent process.

An adversarial evaluation agent is a specialised agent designed to audit, pressure-test and refine the outputs of other autonomous AI agents. Instead of executing tasks, it acts as a digital devil's advocate — simulating friction to catch errors, hallucinations or security flaws before any decision is executed.

Hover a stage to inspect · Scroll through the nine-step audit
01
Parse and Represent
The agent first interprets the content.
  • Extracts claims, assumptions, and structure
  • Identifies factual statements vs. opinions
  • Maps dependencies between ideas
  • Detects implicit premises
Goal: Build an internal model of what is being argued or asserted
02
Establish Evaluation Criteria
It determines what "failure" would look like. Examples:
  • Logical inconsistency
  • Factual error
  • Bias
  • Missing evidence
  • Ambiguity
  • Policy violation
  • Security exploitability
Goal: Define attack surfaces
03
Generate Attack Hypotheses
This is where adversarial reasoning begins. It asks:
  • What if the core premise is false?
  • What edge cases break this?
  • What counterexamples exist?
  • What hidden assumptions are vulnerable?
Typical methods:
  • Contradiction search
  • Boundary testing
  • Worst-case scenario simulation
  • Counterfactual generation
Goal: Produce candidate critiques
04
Stress-Test Claims
The agent actively probes the content. Processes include:
  • Testing internal consistency
  • Comparing against known facts
  • Looking for unsupported leaps
  • Checking robustness under alternate interpretations
Example: A claim like "all effective leaders are charismatic" gets stress-tested with counterexamples.
Goal: Measure resilience
05
Search for Exploitable Weaknesses
The adversarial mode intensifies. This can include:
  • Finding loopholes
  • Spotting vague language
  • Detecting overgeneralizations
  • Identifying manipulation vectors
  • Revealing hidden contradictions
In AI safety, this often resembles red-teaming.
Goal: Maximize failure discovery
06
Rank Critiques by Severity
Not all flaws matter equally. A typical ranking:
  • Critical (breaks the whole argument)
  • Major (significant weakening)
  • Minor (stylistic or local issue)
  • Cosmetic
Goal: Prioritize high-impact weaknesses
07
Construct Counterarguments
The agent formulates explicit opposition. This may involve:
  • Presenting alternative explanations
  • Producing stronger rival hypotheses
  • Reframing the problem
Goal: Test whether the original content survives direct competition
08
Iterative Refinement
Advanced adversarial systems loop.
Cycle: Critique → revise model → attack again → deepen critique.
This often uncovers second-order issues:
  • Assumptions behind assumptions
  • Recursive inconsistencies
Goal: Increase critique depth
09
Synthesize Findings
Finally, it organizes results into usable output. Usually:
  • Summary of weaknesses
  • Evidence for each weakness
  • Confidence level
  • Suggested fixes (optional)
Goal: Convert adversarial analysis into actionable feedback

Nine passes of structured doubt. The Investor Memorandum only advances once every attack surface has been probed, ranked and survived.