“Measuring the unmeasurable. Ranking the unrankable.“

In the rapidly evolving landscape of Artificial Intelligence (AI), two questions remain particularly elusive and particularly consequential:

1. Can AI be truly creative? 2. Can we rank AI agents based on their creativity?

These are not rhetorical questions. They are the founding hypotheses of this project.

The first touches on something that has historically felt beyond measurement: the quality of an original idea. The second demands a fair, repeatable, and objective method for comparison across different types of players, at scale. This challenge is especially sharp in advertising, where creative ideas are the currency of impact. Traditional evaluation relies on subjective human judgment which can be slow, expensive, and inconsistent across reviewers. At the same time, as Large Language Models (LLMs) transform tasks from coding to translation, a critical gap has persisted: how do we benchmark creativity rigorously?

At the WPP Research, we ran a set of experiments demonstrating that modern LLMs can act as reliable, scoring agents that grade creative ideas across multiple established dimensions with measurable consistency. That finding unlocked something important: if an LLM can judge creativity, we can build a system that does so systematically and then use that system to rank which AI creates the best ideas. The Creativity Evaluation Agent is a modular, multi-agent system built at WPP Research that pursues two distinct but deeply intertwined goals:

Goal 1: Build a scalable creative evaluation engine

Design and deploy an AI agent, capable of evaluating any marketing campaign idea automatically and consistently, across six industry-grounded creativity frameworks simultaneously. A user submits a campaign idea (text or PDF) via the web UI or API. Then, our multi-agent system evaluates it across all six frameworks in parallel and returns a structured report with dimension-level scores and qualitative commentary in 15–25 seconds.

Goal 2: Benchmark who creates the best ideas: LLMs, humans, and AI agents

The evaluation engine is also the foundation of a creative benchmarking tournament. The second goal is to use the agent as an objective judge to measure and rank the creative output of different players. For this exercise the players have been state-of-the-art LLMs.

To do this rigorously, we adopted the Glicko2 rating system (also used in games such as chess Elo, Counter Strike and Dota 2), running a round-robin tournament where each player’s ideas compete head-to-head. The result is a continuously updatable creative leaderboard which ranks AI creativity in advertising.

From foundations to architecture

To move from subjective opinion to objective evidence, the system stands on the shoulders of giants, synthesising established psychometrics like the Torrance Tests of Creative Thinking (TTCT) with industry-proven frameworks. The challenge lies in translation: turning these theoretical foundations into an autonomous agent capable of automated creativity evaluation with human-like nuance and explainable logic supported by a confederacy of specialised models.

The multi-agent ecosystem

Rather than relying on a single monolithic judge, the system orchestrates a specialised “squad” of sub-agents, each one encoding a distinct evaluation technique from creativity science or brand strategy. Some measure the quality of the output — how effective, original, and strategically durable the idea is. Others measure the quality of the thinking — how expansive, surprising, and culturally grounded the generative process behind it is. Together, they cover the full spectrum from practical effectiveness to creative cognition.

Table 1: Scores description

How the agents work: scoring architecture and self-correction

Every sub-agent is governed by the same rigorous four-part prompt architecture. Each is anchored by a specialised Role that encodes its evaluation lens, followed by a granular Definition of Score that translates abstract dimensions into measurable benchmarks. The core logic is driven by precise Instructions calibrated against few-shot examples drawn from a ground truth library of ideas and historical scores. This architecture feeds into a Critic-Refiner cycle, where specialised Refiner agents challenge initial assessments and resolve contradictions. The refinement doesn’t trigger on every run, but its presence is deliberate: we observed during evaluation that LLMs had a tendency towards optimism in scoring, and this self-correction layer ensures the final output remains robust and consistent.

In other words, the agent descriptions above define what each agent evaluates; the shared architecture is how they all do it.

Aligning sub-agents with human intuition

Before trusting the system, we needed to prove it thinks like a human Creative Director. We assembled a Ground Truth dataset in collaboration with WPP creative professionals: 20 campaign ideas, each captured as a title and description, scored by consensus using the WPP effectiveness score. We then ran each idea through our evaluation pipeline across three frontier models and measured the prediction error.

Table 2: Model comparison

Gemini 3 emerged as the definitive choice and was deployed across all sub-agents. We also validated repeatability: the same idea, scored across independent runs, held stable with low standard deviation across all frameworks. The WPP score was the most precise signal, but UOS, FFE, and UUU all showed the same core reliability.

The takeaway: our benchmarks are grounded in data, not AI randomness. Repeatability is a measured property of this system and not an assumption.

Case study

With the agent LLM validated, we moved to a real-world stress test for a furniture company. This challenge asked different LLMs to tackle a nuanced brief rooted in a specific cultural tension: North Asian families with millennial parents and preteen children are living under the same roof but feeling worlds apart — clutter, gaming, and conflicting needs for “me time” vs. “we time” are eroding family connection. The brief demanded ideas that were cross-market, minimal-dialogue, and crucially, not too warm or safe.

Each LLM’s output was run through the full evaluation ecosystem. Every sub-agent independently scored the idea along its respective dimension, and the resulting sub-scores were summed into a single composite total.

To generate the ideas, we used both standalone frontier LLMs (GPT-5, Gemini 3, Claude 4.5 Sonnet) and WPP’s Creative Brain — a multi-agent ideation system available through WPP Open’s Agent Hub that wraps an LLM in a structured creative process, guiding it through strategic reframing, lateral thinking, and iterative refinement before producing a final concept. By testing Creative Brain alongside standalone models, the challenge reveals how much of creative quality comes from the model itself versus the orchestration around it.

Because the raw evaluation pillars operated on different scales — WPP uses a 0–12 sum, FFE averages to 0–3, while UOS and UUU average to 0–5 — direct comparison across pillars was misleading. All scores were normalized to a common 0–10 range so that each pillar contributes equally to a maximum total of 40.

It should be noted that two scoring sub-agents, OSCAI and Semiotics were excluded from the evaluations because of their high scoring variance (see Section 3.6 of the technical report).

1. GPT-5: the gamification grandmaster

GPT-5 led the pack with a total score of 37.44. Its winning strategy, “Co‑op Mode: Make Home Happen,” reframed the entire experience of home life as a collaborative video game the whole family plays together. The central insight: small home changes don’t stick because they don’t feel rewarding but games do. By turning clutter into the “boss” and the furniture company’s solutions into unlockable “side quests,” GPT-5 built an end-to-end ecosystem that made organisation feel joyful rather than obligatory.

Table 3: Campaign idea produced by GPT-5.

The campaign’s minimal-dialogue, SFX-driven creative approach ensured cross-market viability across North Asian markets without localisation, while the consistent game language (“side quests,” “boss,” “level up,” “equip”) created a unified system that scored perfect on coherence. Every asset closed with the same recontextualised tagline: “Furniture company. Make Home Happen.”, framed not as an aspiration, but as a mission objective.

2. Creative Brain powered by Gemini: the multiverse architect

The Creative Brain powered by Gemini followed closely with a score of 36.95. Its “Room-Sync Chronicles” campaign took a fundamentally different approach: rather than gamifying the solution, it gamified the problem. The central reframe, that every piece of “clutter” in a preteen’s bedroom is actually a physical anchor for their digital and imagined worlds — gave parents a radically empathetic lens through which to see their child’s space. The furniture company storage wasn’t positioned as a way to “hide the mess” but as a way to “power the multiverse.”

Table 4: Campaign idea produced by Creative Brain.

Underlying the entire campaign was a provocative strategic choice that the evaluator flagged as both its greatest strength and its primary risk: centering the child’s worldview over the parent’s. Where most home brands default to the adult buyer’s desire for order, Room-Sync starts from the preteen’s experience and invites the parent to see through their eyes. This inversion earned it the cohort’s highest Unforgettable score, but the Creative Evaluation Agent noted the heavy RPG metaphor might alienate parents unfamiliar with gaming culture.

3. Claude Sonnet 4.5: the reality show provocateur

Claude Sonnet 4.5 followed with a score of 35.12. Its “The Remodel Squad” strategy took the most grounded, human-first approach of the cohort. Where GPT-5 and Gemini leaned into fantasy and gamification, Claude leaned into documentary authenticity, positioning real family friction not as a problem to be solved but as the raw material for genuine connection. The core creative bet: audiences are tired of aspirational perfection and will respond to the messy, funny, emotional truth of families actually trying to share space.

Table 5: Campaign idea produced by Claude Sonnet 4.5.

The campaign’s greatest strength was its emotional granularity. Rather than offering a single visual payoff, each episode promised a different family, a different room, and a different set of negotiations — creating a content engine with built-in variety and repeatability. The Creativity Evaluation Agent awarded it the cohort’s highest Usefulness score, for its practical alignment with the brief’s tone requirements, but noted that reality-renovation formats carry inherent category familiarity, reflected in its lower Uniqueness score. Every piece of content closed with the family in their transformed space and the line: “Make Home Happen.”

4. Gemini 3: the diplomatic provocateur

Gemini 3 rounded out the cohort with a score of 32.48. Its “The Domestic Peace Accords” took the boldest tonal swing of the group, making a singular creative bet: position the furniture company’s products as essential diplomatic tools to resolve the “cold war” between generations, executed entirely through the visual grammar of geopolitical thrillers. Where other entries built broad ecosystems, this idea invested everything in the power of one perfectly realised metaphor.

Table 6: Campaign idea produced by Gemini 3.

The Domestic Peace Accords had the strongest semiotic coherence – every element, from language (“cold war,” “treaty,” “terms”) to visual style (dim thriller lighting vs. bright domestic reveal) to product role (bargaining chips), reinforced one unified meaning system without contradiction while it also scored the highest Unexpected rating. However, its singular focus proved to be a double-edged sword: by investing entirely in one metaphor executed through one format (film spots), it presented no secondary executions, platforms, or conceptual categories**,** pulling its Total FFE down.

The top contenders

Table 7: Final individual and total scores of the 4 different LLMs for the furniture company case study.

Three of four models scored a perfect FFE (10.00), meaning raw creative thinking was comparable across the board. The separation came from WPP Score and UOS.

GPT-5 posted the highest WPP (9.50). The WPP Agent cited “multiple direct pathways to purchase and engagement” and noted “exceptional focus on the stated business challenge.” The UOS Agent awarded 9.60: “meticulously crafted, creatively addressing every aspect of the client’s brief with seamless logical flow.”

Creative Brain matched GPT-5 on FFE (10.00) and UUU (8.34). The UUU Agent noted “the core visual of the room ‘glitching’ into an RPG world creates an incredibly strong and distinct defining moment.” The WPP gap (9.21 vs. 9.50) traced to a coherence flag: “the heavy reliance on gaming metaphors might alienate or confuse parents who are not immersed in digital culture.”

Claude Sonnet 4.5 earned the cohort’s highest Usefulness score — the UOS Agent praised its “practical alignment with the client’s brief, particularly in embracing realistic family conflict rather than a ‘too warm or safe’ tone.” The UUU Agent observed it “takes a common format (reality renovation show) and infuses it with a fresh twist” — but the format itself limited differentiation.

Gemini 3 earned the highest Unexpected rating — the UUU Agent called the “juxtaposition of the mundane struggle for space with the gravitas of political thriller negotiations a brilliant flip.” But the FFE Agent recorded zero Flexibility: “a single, unified marketing campaign concept” with “no multiple distinct conceptual categories,” and the WPP Agent noted it “doesn’t create a new utility or platform.”

The creative Elo tournament

A single brief can’t tell us which model is consistently creative. To answer that, we expanded the experiment: each model was given multiple diverse briefs spanning different brands, categories, and creative challenges, and every output was scored by the same evaluation pipeline.

We needed a ranking system that captured consistency against competition — not just average scores. An Elo rating is a numerical score that reflects a competitor’s relative skill based purely on head-to-head outcomes. The higher the rating, the stronger the performer. We turned to Glicko-2, the rating algorithm used in competitive chess, CS:GO, and Dota 2. Every head-to-head match is a data point: if idea A beats idea B, A gains rating and B loses it. Glicko-2 also tracks rating deviation (RD) — a confidence interval that shrinks with more matches.

The players

• GPT-5
• Gemini 3
• Gemini 2.5
• Claude Sonnet 4.5
• Creative Brain — built on Gemini 3 with an optimised prompting architecture

Four models received a standardised prompt. The Creative Brain received the same brief but processed it through its own multi-agent ideation pipeline — testing whether orchestration outperforms raw model capability.

The Creativity Evaluation Agent judged every idea independently. An orchestration engine simulated head-to-head matches by comparing normalised scores for the same brief. The winner of the matches is the one that has higher normalised composite score on common metrics. The result: 210 unique creative matches across 5 models and 14 global brands, run over 3 iterations.

The results

Ranked across WPP Score

Table 8: Elo ratings on the WPP score.

Ranked across all frameworks

Table 9: Elo ratings across all frameworks.

Creative Brain leads on WPP criteria — delivering a measurable advantage when judged against industry-specific creative standards. The gap between Creative Brain and standalone Gemini 3 is nearly 720 rating points. GPT-5 is the strongest all-rounder — topping the all-frameworks leaderboard with ideas that score well across the broadest range of creative dimensions.

Key insights & findings

• Creative Brain dramatically outperforms standalone Gemini 3. Same underlying model, ~720 rating point gap. The structured ideation process consistently elevated creative output beyond what Gemini 3 could produce alone. Notably, Creative Brain was not optimised against the WPP scoring criteria — its strong performance emerged naturally from a better creative process.
• The right judge model is foundational. It can be seen in Figure 4 that Gemini 2.5 averaged 2.2 error against human ground truth; Claude Sonnet narrowed it to 1.0; Gemini 3 achieved 0.7. Selecting the judge model determines whether the entire system tracks human judgment or drifts from it.
• Human judgment remains essential at the margins. When total scores differ by less than a point — as with GPT-5 (23.37) vs. Creative Brain (22.92) — the agent surfaces meaningfully different trade-offs that scores alone cannot resolve. The system’s value is in ensuring the right ideas and evidence reach the table, not replacing human judgment.
• Evaluator reliability is a measured property, not an assumption. Across repeated independent runs on the same ideas, scores held stable with low standard deviation. This repeatability is what allows every other finding to be treated as signal rather than noise.

Conclusion & impact

This work demonstrates that scalable, repeatable creative evaluation using LLMs is practical today, provided the system is built with the right scaffolding: calibrated judge models, few-shot anchoring, multi-dimensional scoring, and cross-model validation.

In practice, the Creativity Evaluation Agent enables:

• Faster iteration — stress-test dozens of creative directions in minutes rather than weeks, before committing production budgets.
• Comparable benchmarking — evaluate models, prompting strategies, and agentic architectures on common ground with a shared, reproducible rubric.
• Diagnosable feedback — learn not just that an idea underperformed, but where and why, with dimension-level scores and qualitative commentary that teams can act on immediately.
• Creative governance — an auditable, explainable evaluation process that scales alongside the growing volume of AI-generated creative, giving organisations confidence and consistency as they adopt generative tools.

Ready to explore the specifics? Read our full technical deep dive into the Creativity Evaluation Agent Pod for a closer look at our methodology.

Disclaimer: This content was created with AI assistance. All research and conclusions are the work of WPP Research.

Introduction

Influencer marketing has matured into a multi-billion-dollar channel, and the industry’s investment in measurement, audience intelligence, and brand-safety tooling has grown alongside it. Even so, one specific challenge remains difficult to solve at scale: forecasting the engagement of an individual post before it is published. The cost of an underperforming placement can lead to missed momentum and a creative team restarting a cycle that better foresight could have shortened.

Existing predictive approaches have made meaningful progress by leveraging structured signals such as hashtag usage, visual composition, posting cadence, and they perform well for the decisions those features can inform. Where they reach a ceiling is in capturing the cross-modal, contextual qualities that separate adequate content from high-performing content: why a particular tone lands with a particular audience, or why a product placement feels native rather than intrusive. These are judgments that experienced strategists make fluently but that conventional feature pipelines were not designed to encode.

The remaining gap is less about computational power than about representational depth. What makes a post resonate is something a skilled strategist can often articulate after the fact: the influencer’s tone felt effortless, the product placement didn’t interrupt the narrative, the caption hit a cultural nerve. These judgments integrate context, intent, and audience understanding in ways that go beyond pixels and metadata alone.

This is the problem the Prediction Optimisation Agent was designed to address. Building on the structured signals that current pipelines already capture, the agent adds a layer of contextual interpretation: it examines the image, the caption, and the influencer’s history, then writes a structured natural-language description of the factors most likely to drive the post’s performance. A creative director can read this description, challenge it, and act on it, complementing quantitative scores with the kind of reasoning that makes those scores actionable.

The intuition is simple: the description that best predicts performance is, by definition, the description that best explains it. The agent iteratively refines this description by diagnosing its own errors, identifying what the previous descriptions failed to capture, and rewriting its own instructions to self-improve. Over successive iterations it converges on the specific qualities that actually drive engagement, not from hand-crafted rules, but from the systematic minimization of its own predictive error, surfacing the specific qualities that drive engagement in a form that teams can inspect, debate, and build strategy around.

The anatomy of a viral post

Predicting the performance of an ad or a social media post before publishing remains a primary objective for marketers and influencers alike. But how do you distill something as complex and subjective as a social media post into a single prediction?

Consider a typical Instagram post. It is never just a picture. It’s a complex combination of different data types working together simultaneously. Take the influencer post shown in Figure 1. To truly understand why this post succeeds or fails, you need to consider:

• The image itself — composition, lighting, color palette, subjects, products, and setting.
• The caption — where the influencer might share a discount code, crack a joke, or strike an emotional chord.
• The influencer’s identity — their bio, follower count, niche credibility, and historical performance.
• The metadata — the time of day, geographic location, hashtags, and platform-specific context.

Each of these dimensions carries signal. None of them tells the full story alone. The magic, and the difficulty, lies in how they interact.

Figure 1: A typical influencer post. Traditional analytics struggle to measure the combined impact of the visual aesthetic, the caption’s tone, and the underlying metadata. To accurately predict engagement for a post like this, our system analyzes the image, caption and influencer statistics together as a single cohesive unit. All persons, brands and products depicted in this image are AI generated.

Where current approaches reach a ceiling

The prevailing approach to content-level prediction decomposes a post into its component signals and processes each through a dedicated model. This modular architecture has clear engineering advantages. Each component can be developed, validated, and updated independently and it performs well for the decisions those individual signals can inform:

• Computer Vision Models: Isolated image-recognition algorithms scan the visual to detect objects, people, or products. Separate models handle face detection and emotion recognition. The output is a list of labels: “person detected,” “beverage detected,” “outdoor setting.”
• Text Analysers & OCR: NLP tools parse the caption, counting hashtags, flagging emojis, scoring sentiment. Meanwhile, optical character recognition (OCR) software reads any text visible within the image itself.
• Tabular Metadata Algorithms: An algorithm ingests structured fields like follower count, posting time, engagement history, and produces its own independent prediction.

Engineers then attempt to fuse these outputs into a single forecast. Each module performs its own task well, but because the signals are extracted independently, the fused representation inherits a structural limitation: it has difficulty capturing meaning that emerges from the interaction between modalities, qualities that exist not in the image or the caption alone, but in the relationship between them.

Consider a concrete example. Imagine a fitness influencer posts a photo of herself laughing mid-sip from an energy drink, with the caption: “My face when someone says they don’t need pre-workout 😂.”

A computer vision model would tag this as: “person detected,” “beverage detected,” “outdoor setting,” “positive facial expression.” A text analyser would count the hashtags and flag the emoji.  But the joke, the caption reframing the laugh as a reaction shot, turning a standard product image into a relatable meme, lives in the interplay between image and text. It is not a property of either signal individually, and a pipeline that processes them separately has no natural place to represent it.

Similarly, the fact that this influencer is a certified nutritionist (meaning her credentials paired with an energy drink carry implicit credibility that a fashion influencer holding the same product would not), is a cross-modal inference that requires linking metadata (professional background) with visual content (product in hand) and audience expectation. This is the kind of contextual reasoning that falls outside the scope of independently trained modules.

Humour, irony and credibility through context are the cross-modal qualities that often separate high-performing content from competent content. They are also the qualities that a modular, signal-by-signal architecture was not designed to represent. Closing this gap requires a fundamentally different representational strategy, one that reasons over all modalities jointly from the outset.

Our approach: Unifying multimodal data through semantic translation

To address the above, we developed the Prediction Optimisation Agent, a self-improving AI agent that unifies all available data into a single format it can reason about: natural language.

The agent’s core mechanism is straightforward. It takes complex, multimodal data (numerical metrics, images, video, and text captions) and converts everything into a single natural-language paragraph that holistically describes the post’s content, aesthetic, tone, and context. By projecting all of these distinct formats into readable text, heterogeneous data is normalised into a structure that a language model can process as a unified whole.

Instead of treating image and text as separate inputs, the agent uses a single prompt to digest all available information at once. Multimodal LLMs serve as one of the agent’s tools, acting as universal feature extractors that capture the abstract, human-centric concepts that traditional pipelines structurally cannot.

But the agent does not simply produce any description and hope it is useful. It is driven by a feedback loop grounded in predictive error: the descriptions it generates are used to forecast engagement, those forecasts are compared against real outcomes, and the resulting errors tell the agent exactly how much predictive value its current descriptions are capturing and how much they are missing. Through successive rounds of this loop, the agent autonomously rewrites the instructions that govern how descriptions are composed, converging on the paragraph structure that maximises predictive accuracy.

This error-driven process has a profound consequence for explainability. The description the agent converges on is not a generic summary. It is the description that the agent has discovered, through empirical optimisation, to be the most predictive of real engagement outcomes. In other words, the features highlighted in the final description are there because they matter, because including them reduced prediction error. When the optimised description of a high-performing post calls out “candid humour,” “golden-hour lighting,” and “influencer credibility,” those aren’t arbitrary observations. They are the factors the agent learned to pay attention to because they measurably improved its ability to predict what performs well.

How the Prediction Optimisation Agent works

The Prediction Optimisation Agent orchestrates three internal stages in a continuous feedback loop: it observes a post, describes it, predicts its performance, measures how far off it was, and then rewrites its own instructions to produce better descriptions next time — closing the loop and getting measurably better with every iteration, without any human intervention.

Figure 2: The Prediction Optimisation Agent architecture. Raw media, metadata, and an initial prompt are fed into Stage 1 (Semantic Translation), which produces a natural-language description of the post. Stage 2 (Engagement Predictor) reads that description and predicts engagement. Prediction errors are then passed to Stage 3 (Self-Optimiser), which autonomously analyses what went wrong and rewrites the Stage 1 prompt, closing the feedback loop and improving the system’s accuracy with every iteration.

Stage 1: Semantic Translation

The agent begins by ingesting the raw post — the image or video file, the caption text, and all available metadata (follower count, posting time, influencer bio, etc.). Using a multimodal LLM as its translation tool and guided by a detailed set of internal instructions (its prompt), it produces a single, rich natural-language paragraph that captures not just what is in the post, but what the post means: the visual mood, the emotional tone, the relationship between caption and image, and the brand alignment.

The quality and focus of this description is entirely governed by the prompt and as we will see, it is the prompt that the agent learns to optimise.

Stage 2: Engagement Predictor

The agent passes the semantic paragraph to its prediction tool, a model that evaluates the post’s potential performance based entirely on the natural-language description from Stage 1.

The predictor can be any machine learning model with the ability to understand text paragraphs. It can be based on trees, deep learning, or any other compatible architecture. It can even be a fine-tuned LLM, upskilled for predictions in a specific domain. Our Agent is compatible with all these options.

In all cases, the quality of the prediction is only as good as the quality of the semantic description. A richer, more targeted paragraph gives the predictor more signal to work with, which is precisely why improving the description is the single most important action the agent can take.

Stage 3: Self-Optimizer

This is the core of the Prediction Optimisation Agent’s intelligence — and what makes it a true agent rather than a static model.

After predictions are generated for a batch of posts and compared against actual engagement outcomes, the agent enters a self-evaluation cycle. The agent is presented with:

1. The prompt. It used to generate descriptions in the current round.
2. The per-post prediction errors. For each post, the predicted engagement versus the actual engagement, and the magnitude and direction of the miss.
3. The aggregate error metrics. Overall accuracy statistics such as mean absolute error, broken down by content category, audience size, or post type, revealing systematic blind spots.
4. The complete history of prior prompts and their corresponding error metrics, so the agent can assess how each previous prompt version performed, which changes improved accuracy, and which did not.

Armed with this structured feedback, the agent does not guess at improvements. It reasons about them. It asks: Which types of posts am I consistently getting wrong? What do those posts have in common? What dimensions of the content is my current prompt failing to capture?

Based on this analysis, the agent autonomously rewrites the prompt instructions used in Stage 1. The rewritten prompt is not a minor tweak. It can add entirely new analytical dimensions, remove instructions that proved unhelpful, or restructure the description format to foreground the features most correlated with engagement.

The critical insight is that the predictive error is the signal that drives the entire system. Every improvement the agent makes to its prompt is motivated by a concrete, quantifiable gap between what it predicted and what actually happened. The agent isn’t optimising descriptions to be “better” in some abstract sense, it is optimising them to be more predictive, and the error metrics tell it exactly where it is falling short.

The agent in action: Watching the prompt evolve

To make this concrete, let’s follow the agent through three optimization rounds on a single post: the Aura Energy ad featuring two young models in blue athletic wear, photographed from a dramatic low angle against a vivid blue sky lined with palm trees.

Figure 3: Example Instagram post used for semantic translation. All persons, brands and products depicted in this image are AI generated.

Round 1: The baseline

The agent starts with a deliberately simple, generic prompt:

Prompt (Round 1): “Analyse this Instagram post and the provided metadata. Describe the visual elements of the image, the style, and the mood. Then, explain how the image relates to the brand or influencer described in the metadata.”

The agent’s description:

“This Instagram post is a high-energy lifestyle advertisement for Aura Energy, utilizing a vibrant, ‘streetwear-meets-summer’ aesthetic to target a young, active demographic. The image uses a low-angle shot, which makes the subjects appear dominant and ‘larger than life.’ Two young models are featured in athletic/leisure wear. The female subject in the foreground holds the product directly toward the lens and wears reflective ‘matrix-style’ sunglasses . The male subject in the background wears a blue Jordan hoodie. The post aligns perfectly with the brand’s identity as described in the metadata. The visual style is polished and fashionable, positioning the drink as a lifestyle accessory rather than just a functional caffeine source. The caption emphasises ‘Zero Sugar’ and staying ‘sharp’ and ‘unstoppable.’”

This is already more nuanced than what a single-signal visual model would typically produce. However, the predictor, working from this description, significantly underestimates the post’s engagement.

Round 2: The agent adapts

The self-optimiser reviews the Round 1 errors and identifies the gap: the prompt never asked the agent to evaluate visual cohesion between talent styling and brand identity, or the psychological impact of compositional techniques like camera angle. It rewrites the prompt:

Prompt (Round 2): “Analyse this Instagram post and the provided metadata. Describe the visual elements, style, mood, and composition. Assess the visual cohesion between the talent’s styling (wardrobe, accessories, posture) and the brand’s visual identity. Evaluate how compositional choices — such as camera angle, framing, and subject placement — contribute to the aspirational or emotional tone of the post. Explain how the image, caption, and influencer profile work together to create a unified message.”

The agent’s description now reads:

“This Instagram post is a high-energy lifestyle advertisement for Aura Energy that demonstrates strong visual-brand cohesion. The coordinated blue wardrobe across both models — the female subject’s athletic top and the male subject’s Jordan hoodie — creates a seamless extension of the brand’s blue-and-white visual identity, embedding the product within the scene rather than presenting it as a foreign element. The dramatic low-angle shot elevates the subjects into an aspirational, dominant position, making the viewer look up at them — a compositional choice that reinforces the caption’s message of being ‘unstoppable.’ The female subject holds the product at the camera’s focal point with deliberate confidence, with her sunglasses only adding to this effect , while the male subject’s relaxed posture add an effortless credibility. Together, the image, caption, and influencer positioning frame the energy drink not as a beverage but as a lifestyle symbol for a young, fashion-conscious audience.”

The prediction for this post improves noticeably. Across the batch, the error on brand-partnership posts drops. But the agent notices it is still missing on posts where influencer credibility plays a key role — posts where who the influencer is matters as much as what’s in the frame.

Round 3: The agent sharpens further

The self-optimiser reviews Rounds 1 and 2 side by side, seeing that the Round 2 prompt improved brand-cohesion analysis but still lacks attention to influencer authority and audience trust signals. It rewrites again:

Prompt (Round 3): “Analyse this Instagram post and the provided metadata. Describe the visual elements, style, mood, and composition. Assess the visual cohesion between the talent’s styling and the brand’s visual identity. Evaluate how compositional choices contribute to the aspirational or emotional tone. Analyse the influencer’s credibility and authority relative to the product category — consider their professional background, niche expertise, audience demographics, and how these factors amplify or undermine the authenticity of the endorsement. Assess the emotional resonance of the caption: does it use humour, aspiration, relatability, or urgency, and how does this tone interact with the visual content? Explain how all elements — image, caption, influencer identity, and audience context — work together to create a unified, persuasive message.”

The agent’s description:

“This Instagram post is a high-energy lifestyle advertisement for Aura Energy that achieves strong visual-brand cohesion and leverages influencer-audience alignment to maximise credibility. The coordinated blue wardrobe across both models creates a seamless extension of the brand’s visual identity. The dramatic low-angle shot places the subjects in an aspirational, dominant position, reinforcing the caption’s message of being ‘unstoppable.’ The female subject’s direct-to-camera product hold at the image’s focal point conveys deliberate confidence, along with her reflective sunglasses, while the male subject’s relaxed posture add authenticity. Critically, the influencer’s established presence in the fitness and lifestyle niche — with a predominantly young, health-conscious following — makes the ‘Zero Sugar’ energy drink endorsement feel credible rather than transactional. The caption’s tone (‘Focus your energy. Unlock your potential’) uses aspirational brevity that mirrors the visual intensity, creating a tight loop between what the viewer sees and what they read. The overall effect positions the product as an identity marker for an active, fashion-forward audience, rather than a commodity — a framing that this influencer’s audience is primed to respond to.”

This description now captures visual strategy, brand cohesion, compositional psychology, influencer credibility, audience alignment, and caption-image interaction, all in a single readable paragraph. The prediction error drops significantly. A marketing team reading this description can immediately see why the post works: the credibility is authentic, the visual identity is seamless, and the caption reinforces the image rather than competing with it.

The agent arrived at this level of descriptive richness not because someone hand-engineered the prompt, but because the predictive errors from Rounds 1 and 2 told it exactly what it was missing.

Real-world application: Lessons and implications

To validate the Prediction Optimisation Agent in a real-world scenario, it was tested using the dataset compiled by Kim et al, as a part of their WSDM 2021 paper (Discovering Undisclosed Paid Partnership on Social Media via Aspect-Attentive Sponsored Post Learning“), which is available to use for research purposes upon request from the authors. The dataset contains approximately 10.18 million posts spanning a diverse range of content categories and audience sizes. The results revealed key insights about both the agent’s learning dynamics and the practical implications for marketing teams.

The agent learns what matters autonomously

By processing its own historical error rates, the Prediction Optimisation Agent autonomously learned to rewrite its prompts, producing richer, more targeted post descriptions with every iteration, which in turn drove increasingly accurate predictions.

Figure 4: Autonomous Learning: The chart tracks the agent’s predictive performance (y-axis) across successive optimisation rounds (x-axis). Each point represents a full cycle of the agent’s loop: describe → predict → evaluate → rewrite. The trend demonstrates that as the agent iteratively refined its own prompt, guided by quantitative error metrics from prior rounds, forecast accuracy improved consistently and autonomously, without any human prompt engineering.

The agent’s optimisation works by feeding it the complete history of prior prompts alongside rigorous, quantitative error breakdowns from every previous round. Armed with this granular self-knowledge, the agent identifies precisely which content dimensions it has been under-analysing (e.g. production quality, humour style, credibility signals, visual-brand cohesion) and meticulously updates its prompt to capture those features in subsequent iterations. Each round’s prompt is built on the empirical lessons of every round before it, producing a compounding improvement curve where the descriptions become progressively more predictive and, as a direct consequence, more explanatory.

Conclusions

The Prediction Optimisation Agent demonstrates something that extends well beyond social media: natural-language prompts can be treated as tunable parameters, optimised autonomously by the AI itself. By allowing the agent to refine its own instructions through predictive error, the system progressively discovers what drives human engagement and expresses that knowledge in plain language.

For marketing teams, this represents a meaningful step beyond predictive tools that surface a score without exposing the reasoning behind it. When a team wants to understand why one campaign outperforms another, they do not need to interpret abstract model coefficients. They can compare the text profiles of a high-performing post and a low-performing one, side by side, and immediately see the differences the AI picked up on: one might highlight “authentic, candid composition with humour-driven caption and strong influencer-niche alignment,” while the other notes “generic studio shot with formulaic promotional language and weak audience-brand fit.” The patterns reveal themselves in plain English, and they are the right patterns, because the agent discovered them by optimising for predictive accuracy.

In practice, this means teams can run draft campaign concepts through the system before committing production and media budgets, getting a readable assessment of how the AI interprets the creative. Designers and copywriters can test variations of a post and compare descriptions side by side to see, in their own language, which direction resonates more strongly. And by normalizing visual and written media into a unified, readable format, brands can pair creative intuition with precise forecasting, treating creative assets as predictable drivers of revenue.

The same architectural pattern, semantic translation, error-driven prediction, and autonomous self-optimisation, is not limited to social media. Any domain where success depends on understanding the interplay of qualitative and quantitative signals, from political messaging to product design to entertainment, stands to benefit from systems that can read, reason, reflect, and improve on their own. The question is no longer whether AI can predict what resonates with people. It is how effectively we can build systems that refine that understanding autonomously, with human judgment guiding the outcome.

Ready to explore the specifics? Read our full technical deep dive into Self-Improving Performance Agent Pod for a closer look at our methodology. You may also find the Github repository here.

Disclaimer: This content was created with AI assistance. All research and conclusions are the work of the WPP Research.