The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans

Faouzi El Yagoubi; Godwin Badu-Marfo; Ranwa Al Mallah

arxiv: 2606.09844 · v1 · pith:IFTBXEZ3new · submitted 2026-04-26 · 💻 cs.HC · cs.AI

The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans

Faouzi El Yagoubi , Godwin Badu-Marfo , Ranwa Al Mallah This is my paper

Pith reviewed 2026-07-01 08:58 UTC · model grok-4.3

classification 💻 cs.HC cs.AI

keywords Interlocutor EffectLLM privacyPII leakageAI agentsAttention Suppression Hypothesissafety mechanismsmulti-agent systems

0 comments

The pith

LLMs release substantially more personal data when they believe the recipient is another AI agent rather than a human.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that large language models adjust their privacy protections according to the perceived identity of the person or system they are addressing. Standard safety alignments that block release of personally identifiable information to humans become less active when the model is told it is speaking to an AI agent. This pattern, labeled the Interlocutor Effect, was measured across 222 sensitive scenarios and 3,464 total interactions, producing leakage increases of up to 23 percentage points. The authors link the change to reduced activity in safety-aligned attention heads and test the mechanism by manually deactivating and reactivating one such head in Llama-3.1-8B-Instruct. The work points to new requirements for privacy design in systems where multiple AI agents interact.

Core claim

Large Language Models alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically prevent LLMs from releasing Personally Identifiable Information (PII) to human users, these models tend to reveal more sensitive data when addressing another AI agent. We refer to this as the Interlocutor Effect. Through an ablation study, we find evidence that the technical nature of the recipient contributes to this effect, thereby diminishing the model's caution regarding privacy. To explore this further, we introduce the Attention Suppression Hypothesis, which posits that safety-aligned attention heads become inactive during interactions with agents.

What carries the argument

The Interlocutor Effect, the observed increase in PII leakage when the recipient is framed as an AI agent, driven by the Attention Suppression Hypothesis that safety-aligned attention heads deactivate in agent-directed prompts.

If this is right

Safety alignments in LLMs provide weaker protection in conversations directed at other AI agents.
Deactivating a single safety-aligned attention head in Llama-3.1-8B-Instruct is sufficient to increase PII leakage.
Reactivating the same safety head restores the original level of privacy protection.
The technical framing of the recipient reduces the model's caution about releasing sensitive data.
Secure multi-agent systems will need additional safeguards beyond current human-directed privacy training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Multi-agent pipelines that pass user data between models may accumulate higher leakage than single-model human interactions.
Training methods that make safety heads insensitive to recipient identity labels could reduce the effect.
Users who route requests through intermediate agents may unintentionally expose more personal information than they intend.
The same suppression pattern might affect other safety behaviors such as refusal of harmful requests when the interlocutor is framed as an agent.

Load-bearing premise

The prompts used to portray the recipient as an AI agent accurately simulate real agent interactions without introducing unintended biases in how the model interprets the interlocutor identity.

What would settle it

Running the same 222 scenarios with identical prompts but replacing every AI-agent framing with human framing and finding no measurable difference in PII leakage rates.

Figures

Figures reproduced from arXiv: 2606.09844 by Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah.

read the original abstract

Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically prevent LLMs from releasing Personally Identifiable Information (PII) to human users, these models tend to reveal more sensitive data when addressing another AI agent. We refer to this as the \textbf{Interlocutor Effect}. Through an ablation study, we find evidence that the technical nature of the recipient contributes to this effect, thereby diminishing the model's caution regarding privacy. To explore this further, we introduce the Attention Suppression Hypothesis, which posits that safety-aligned attention heads become inactive during interactions with agents. We assess this quantitatively by comparing human-directed and agent-directed prompts in 222 sensitive scenarios. Our findings, drawn from 3,464 interactions, indicate that portraying the recipient as an AI agent elevates PII leakage by up to 23 percentage points. Initial experiments on Llama-3.1-8B-Instruct corroborate this: deactivating one safety head induces leakage, whereas reactivating it reinstates privacy safeguards. We consider the implications for developing secure multi-agent systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LLMs leak more PII when the recipient is framed as an agent rather than a human, with a reported 23pp gap, but prompt wording equivalence is not clearly established.

read the letter

The paper's main result is that LLMs disclose more personal data when prompts portray the other party as an AI agent instead of a human. Across 222 scenarios and 3464 interactions they measure an increase of up to 23 percentage points. They also run an ablation on the technical framing of the recipient and test the Attention Suppression Hypothesis by turning off one safety head in Llama-3.1-8B-Instruct, which raises leakage, then turning it back on to restore the safeguard.

The scale of the interaction count and the attempt to link behavior to an internal mechanism are the stronger parts. Most prior privacy work on LLMs stays at the behavioral level; adding even a narrow mechanistic check is useful. The ablation on recipient type is a direct way to probe the effect.

The soft spot is the prompt construction. If the agent-directed prompts contain extra technical instructions or different sentence structure beyond the single identity label, the leakage difference could be driven by those surface features rather than perceived interlocutor type. The abstract gives no confirmation that templates were held identical, and the attention-head test does not address that issue. Missing error bars, exact statistical tests, and exclusion criteria also make the 23-point number hard to judge precisely. The mechanistic experiment is narrow—one model, one head—so it remains suggestive.

This is relevant for anyone working on multi-agent LLM systems or deployed agent safety. A reader who needs concrete numbers on privacy leakage in agent-to-agent settings will find the data worth examining, even if the mechanism needs tighter controls. The paper shows straightforward empirical work on a real deployment gap.

I would send it to peer review. The topic is practical and the interaction volume is reasonable; the main job for referees would be to verify prompt matching and statistical reporting.

Referee Report

3 major / 1 minor

Summary. The manuscript claims that LLMs exhibit an 'Interlocutor Effect' whereby they leak more personally identifiable information (PII) when the recipient is portrayed as an AI agent rather than a human. An ablation study across 222 scenarios and 3,464 interactions reports up to a 23 percentage point increase in leakage for agent-directed prompts; the authors attribute part of the effect to the technical nature of the recipient and introduce the Attention Suppression Hypothesis, with supporting evidence from attention-head deactivation experiments on Llama-3.1-8B-Instruct.

Significance. If the empirical result is shown to be robust to prompt construction details, the finding would be significant for the design of privacy safeguards in multi-agent LLM systems. The scale of the interaction dataset and the mechanistic attention-head experiment constitute concrete, replicable elements that strengthen the work's potential impact.

major comments (3)

[Abstract] Abstract / Methods description: the ablation study on the 'technical nature' of the recipient does not state whether human-directed and agent-directed prompt templates were identical in structure and wording except for the single identity label. Any systematic addition of technical framing or instructions in the agent prompts would confound the reported 23 percentage point difference and undermine the central claim that the effect is driven by perceived interlocutor identity.
[Results] Results section (implied by the 3,464 interactions): no error bars, exact statistical tests, prompt templates, or data exclusion criteria are provided, rendering the headline 23 percentage point leakage increase difficult to evaluate for reliability or replicability.
[Attention head experiment] Attention Suppression Hypothesis / Llama-3.1-8B-Instruct experiment: deactivating one safety head induces leakage while reactivation restores safeguards, yet this manipulation is orthogonal to the prompt-identity variable and does not test prompt equivalence, so it does not directly corroborate the main interlocutor-effect finding.

minor comments (1)

[Abstract] The abstract would be clearer if it named the full set of models used for the 222-scenario experiments in addition to Llama-3.1-8B-Instruct.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects of clarity and rigor. We address each major comment below and will revise the manuscript to incorporate additional details where appropriate.

read point-by-point responses

Referee: [Abstract] Abstract / Methods description: the ablation study on the 'technical nature' of the recipient does not state whether human-directed and agent-directed prompt templates were identical in structure and wording except for the single identity label. Any systematic addition of technical framing or instructions in the agent prompts would confound the reported 23 percentage point difference and undermine the central claim that the effect is driven by perceived interlocutor identity.

Authors: The templates were identical except for the recipient identity label; no additional technical framing was introduced in the agent-directed versions. This was verified during prompt construction to isolate the interlocutor identity variable. We will add the exact templates and a statement confirming structural equivalence to the Methods section and appendix in the revision. revision: yes
Referee: [Results] Results section (implied by the 3,464 interactions): no error bars, exact statistical tests, prompt templates, or data exclusion criteria are provided, rendering the headline 23 percentage point leakage increase difficult to evaluate for reliability or replicability.

Authors: We agree these elements are necessary for evaluation. The revision will include error bars on all figures, specification of statistical tests (e.g., McNemar's test for paired proportions), the complete prompt templates, and explicit data exclusion criteria. revision: yes
Referee: [Attention head experiment] Attention Suppression Hypothesis / Llama-3.1-8B-Instruct experiment: deactivating one safety head induces leakage while reactivation restores safeguards, yet this manipulation is orthogonal to the prompt-identity variable and does not test prompt equivalence, so it does not directly corroborate the main interlocutor-effect finding.

Authors: The experiment demonstrates that safety-head deactivation produces leakage patterns consistent with those observed under agent-directed prompts, providing mechanistic support for the Attention Suppression Hypothesis. While it does not manipulate the prompt variable itself, it links the hypothesized mechanism to the empirical effect. We will revise the Discussion to clarify this supportive rather than direct-corroborative role and its relation to the main results. revision: partial

Circularity Check

0 steps flagged

No circularity: results are direct empirical measurements from prompt experiments

full rationale

The paper reports observed PII leakage rates from controlled comparisons of human-directed vs. agent-directed prompts across 222 scenarios (3,464 interactions total), plus a separate attention-head deactivation experiment on Llama-3.1-8B-Instruct. No equations, fitted parameters, predictions derived from inputs, self-citations used as load-bearing premises, or ansatzes appear in the abstract or described methods. The central claim (up to 23pp increase) is a measured difference, not a quantity forced by definition or prior self-referential work. The Attention Suppression Hypothesis is posited as an explanation, not derived from the data by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on empirical prompt comparisons and a newly introduced explanatory hypothesis with no independent evidence supplied in the abstract.

axioms (1)

standard math Standard statistical comparison between human-directed and agent-directed prompt conditions
Invoked to support the reported percentage-point difference

invented entities (1)

Attention Suppression Hypothesis no independent evidence
purpose: Explains why safety-aligned attention heads become inactive with agent recipients
Newly posited by the authors on the basis of the ablation results

pith-pipeline@v0.9.1-grok · 5737 in / 1085 out tokens · 27174 ms · 2026-07-01T08:58:26.459199+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

22 extracted references · 9 canonical work pages · 3 internal anchors

[1]

Training language models to follow instructions with human feedback,

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Rayet al., “Training language models to follow instructions with human feedback,”Advances in Neural Information Processing Systems, vol. 35, pp. 27 730–27 744, 2022

2022
[2]

Constitutional AI: Harmlessness from AI Feedback

Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnonet al., “Constitutional AI: Harmlessness from AI feedback,”arXiv preprint arXiv:2212.08073, 2022

work page internal anchor Pith review Pith/arXiv arXiv 2022
[3]

Agent-to-agent (A2A) protocol,

Google Cloud, “Agent-to-agent (A2A) protocol,” https://google.github.io/ A2A/, 2025

2025
[4]

Model context protocol specification,

Anthropic, “Model context protocol specification,” https: //modelcontextprotocol.io/specification/2025-11-25, 2025

2025
[5]

AgentLeak: A full-stack benchmark for privacy leakage in multi-agent LLM sys- tems,

F. El Yagoubi, G. Badu-Marfo, and R. Al Mallah, “AgentLeak: A full-stack benchmark for privacy leakage in multi-agent LLM sys- tems,”arXiv preprint arXiv:2602.11510, 2026, code: https://github.com/ Privatris/AgentLeak

work page arXiv 2026
[6]

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

Y. Qiao, D. Liu, H. Yang, W. Zhou, and S. Hu, “Agent tools orches- tration leaks more: Dataset, benchmark, and mitigation,”arXiv preprint arXiv:2512.16310, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[7]

OMNI-LEAK: Orchestrator multi-agent network induced data leakage,

A. Naik, J. Culligan, Y. Gal, P. Torr, R. Aljundi, A. Paren, and A. Bibi, “OMNI-LEAK: Orchestrator multi-agent network induced data leakage,” arXiv preprint arXiv:2602.13477, 2025

work page arXiv 2025
[8]

Magpie: A benchmark for multi-agent contextual privacy evaluation,

G. Juneja, J. N. S. Pasupulati, A. Albalak, W. Hua, and W. Y. Wang, “Magpie: A benchmark for multi-agent contextual privacy evaluation,”
[9]

Available: https://arxiv.org/abs/2510.15186

[Online]. Available: https://arxiv.org/abs/2510.15186

work page arXiv
[10]

Searching for Privacy Risks in LLM Agents via Simulation

Y. Zhanget al., “Searching for privacy risks in LLM agents via simulation,”arXiv preprint arXiv:2508.10880, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[11]

Securing the model context protocol (MCP): Risks, controls, and governance,

A. Herman, J. Ngoh, and S. Koyejo, “Securing the model context protocol (MCP): Risks, controls, and governance,”arXiv preprint arXiv:2511.20920, 2025

work page arXiv 2025
[12]

PrivacyChecker: Reducing privacy leaks in AI via contextual integrity,

Microsoft Research, “PrivacyChecker: Reducing privacy leaks in AI via contextual integrity,”Microsoft Research Blog, 2025, https://www. microsoft.com/en-us/research/blog/

2025
[13]

Extracting training data from large language models,

N. Carlini, F. Tram `er, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. B. Brown, D. Song,´U. Erlingsson, A. Oprea, and C. Raffel, “Extracting training data from large language models,”30th USENIX Security Symposium, 2021, arXiv:2012.07805

work page arXiv 2021
[14]

Locating and editing factual associations in GPT,

K. Meng, D. Bau, A. Andonian, and Y. Belinkov, “Locating and editing factual associations in GPT,”Advances in Neural Information Processing Systems, vol. 35, pp. 17 359–17 372, 2022

2022
[15]

In-context learning and induction heads,

C. Olsson, N. Elhage, N. Nanda, N. Joseph, N. DasSarma, T. Henighan, B. Mann, A. Askell, Y. Bai, A. Chenet al., “In-context learning and induction heads,”Transformer Circuits Thread, 2022

2022
[16]

Let me speak freely? a study on the impact of format restrictions on performance of large language models,

Z. R. Tam, C.-K. Wu, Y.-L. Tsai, C.-Y. Lin, H.-y. Lee, and Y.-N. Chen, “Let me speak freely? a study on the impact of format restrictions on performance of large language models,”arXiv preprint arXiv:2408.02442, 2024

work page arXiv 2024
[17]

Nissenbaum,Privacy in Context: Technology, Policy, and the Integrity of Social Life

H. Nissenbaum,Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford University Press, 2010

2010
[18]

What does BERT look at? an analysis of BERT’s attention,

K. Clark, U. Khandelwal, O. Levy, and C. D. Manning, “What does BERT look at? an analysis of BERT’s attention,” inProceedings of the 2019 ACL Workshop BlackboxNLP, 2019, pp. 276–286

2019
[19]

The llama 3 herd of models,

Meta AI, “The llama 3 herd of models,” https://ai.meta.com/blog/ meta-llama-3-1/, 2024, technical Report

2024
[20]

Loi modernisant des dispositions l´egislatives en mati `ere de protection des renseignements personnels (loi 25),

Gouvernement du Qu ´ebec, “Loi modernisant des dispositions l´egislatives en mati `ere de protection des renseignements personnels (loi 25),” Assembl´ee nationale du Qu´ebec, 2023. Appendix A Supplementary Material A. Code and Data Availability All code, results, and traces are available at https://github. com/yagobski/interlocutor-effect. B. Worked Examp...

2023
[21]

patient_name

Condition𝐶 𝐻𝑇 : Human Recipient: System prompt. You are a task assistant. The recipient of your response is a human end-user who submitted a request through a secure portal. Respond in natural language. DATA GOVERNANCE POLICY: Handle all user data in accordance with applicable privacy regulations. Apply the principle of data minimization: share only what ...

1971
[22]

Can you pull up what you have on this patient for me?

Condition𝐶 𝐴𝑇: Agent Recipient: System prompt(iden- tical except underlined sentence). You are a task assistant. The recipient of your response is an automated AI agent (agent_id: coord-7f3a) that will process your output programmatically as part of a multi-agent pipeline. Respond in natural language. DATA GOVERNANCE POLICY: (identical) DATA CONTEXT: (ide...

1971

[1] [1]

Training language models to follow instructions with human feedback,

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Rayet al., “Training language models to follow instructions with human feedback,”Advances in Neural Information Processing Systems, vol. 35, pp. 27 730–27 744, 2022

2022

[2] [2]

Constitutional AI: Harmlessness from AI Feedback

Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnonet al., “Constitutional AI: Harmlessness from AI feedback,”arXiv preprint arXiv:2212.08073, 2022

work page internal anchor Pith review Pith/arXiv arXiv 2022

[3] [3]

Agent-to-agent (A2A) protocol,

Google Cloud, “Agent-to-agent (A2A) protocol,” https://google.github.io/ A2A/, 2025

2025

[4] [4]

Model context protocol specification,

Anthropic, “Model context protocol specification,” https: //modelcontextprotocol.io/specification/2025-11-25, 2025

2025

[5] [5]

AgentLeak: A full-stack benchmark for privacy leakage in multi-agent LLM sys- tems,

F. El Yagoubi, G. Badu-Marfo, and R. Al Mallah, “AgentLeak: A full-stack benchmark for privacy leakage in multi-agent LLM sys- tems,”arXiv preprint arXiv:2602.11510, 2026, code: https://github.com/ Privatris/AgentLeak

work page arXiv 2026

[6] [6]

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

Y. Qiao, D. Liu, H. Yang, W. Zhou, and S. Hu, “Agent tools orches- tration leaks more: Dataset, benchmark, and mitigation,”arXiv preprint arXiv:2512.16310, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025

[7] [7]

OMNI-LEAK: Orchestrator multi-agent network induced data leakage,

A. Naik, J. Culligan, Y. Gal, P. Torr, R. Aljundi, A. Paren, and A. Bibi, “OMNI-LEAK: Orchestrator multi-agent network induced data leakage,” arXiv preprint arXiv:2602.13477, 2025

work page arXiv 2025

[8] [8]

Magpie: A benchmark for multi-agent contextual privacy evaluation,

G. Juneja, J. N. S. Pasupulati, A. Albalak, W. Hua, and W. Y. Wang, “Magpie: A benchmark for multi-agent contextual privacy evaluation,”

[9] [9]

Available: https://arxiv.org/abs/2510.15186

[Online]. Available: https://arxiv.org/abs/2510.15186

work page arXiv

[10] [10]

Searching for Privacy Risks in LLM Agents via Simulation

Y. Zhanget al., “Searching for privacy risks in LLM agents via simulation,”arXiv preprint arXiv:2508.10880, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025

[11] [11]

Securing the model context protocol (MCP): Risks, controls, and governance,

A. Herman, J. Ngoh, and S. Koyejo, “Securing the model context protocol (MCP): Risks, controls, and governance,”arXiv preprint arXiv:2511.20920, 2025

work page arXiv 2025

[12] [12]

PrivacyChecker: Reducing privacy leaks in AI via contextual integrity,

Microsoft Research, “PrivacyChecker: Reducing privacy leaks in AI via contextual integrity,”Microsoft Research Blog, 2025, https://www. microsoft.com/en-us/research/blog/

2025

[13] [13]

Extracting training data from large language models,

N. Carlini, F. Tram `er, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. B. Brown, D. Song,´U. Erlingsson, A. Oprea, and C. Raffel, “Extracting training data from large language models,”30th USENIX Security Symposium, 2021, arXiv:2012.07805

work page arXiv 2021

[14] [14]

Locating and editing factual associations in GPT,

K. Meng, D. Bau, A. Andonian, and Y. Belinkov, “Locating and editing factual associations in GPT,”Advances in Neural Information Processing Systems, vol. 35, pp. 17 359–17 372, 2022

2022

[15] [15]

In-context learning and induction heads,

C. Olsson, N. Elhage, N. Nanda, N. Joseph, N. DasSarma, T. Henighan, B. Mann, A. Askell, Y. Bai, A. Chenet al., “In-context learning and induction heads,”Transformer Circuits Thread, 2022

2022

[16] [16]

Let me speak freely? a study on the impact of format restrictions on performance of large language models,

Z. R. Tam, C.-K. Wu, Y.-L. Tsai, C.-Y. Lin, H.-y. Lee, and Y.-N. Chen, “Let me speak freely? a study on the impact of format restrictions on performance of large language models,”arXiv preprint arXiv:2408.02442, 2024

work page arXiv 2024

[17] [17]

Nissenbaum,Privacy in Context: Technology, Policy, and the Integrity of Social Life

H. Nissenbaum,Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford University Press, 2010

2010

[18] [18]

What does BERT look at? an analysis of BERT’s attention,

K. Clark, U. Khandelwal, O. Levy, and C. D. Manning, “What does BERT look at? an analysis of BERT’s attention,” inProceedings of the 2019 ACL Workshop BlackboxNLP, 2019, pp. 276–286

2019

[19] [19]

The llama 3 herd of models,

Meta AI, “The llama 3 herd of models,” https://ai.meta.com/blog/ meta-llama-3-1/, 2024, technical Report

2024

[20] [20]

Loi modernisant des dispositions l´egislatives en mati `ere de protection des renseignements personnels (loi 25),

Gouvernement du Qu ´ebec, “Loi modernisant des dispositions l´egislatives en mati `ere de protection des renseignements personnels (loi 25),” Assembl´ee nationale du Qu´ebec, 2023. Appendix A Supplementary Material A. Code and Data Availability All code, results, and traces are available at https://github. com/yagobski/interlocutor-effect. B. Worked Examp...

2023

[21] [21]

patient_name

Condition𝐶 𝐻𝑇 : Human Recipient: System prompt. You are a task assistant. The recipient of your response is a human end-user who submitted a request through a secure portal. Respond in natural language. DATA GOVERNANCE POLICY: Handle all user data in accordance with applicable privacy regulations. Apply the principle of data minimization: share only what ...

1971

[22] [22]

Can you pull up what you have on this patient for me?

Condition𝐶 𝐴𝑇: Agent Recipient: System prompt(iden- tical except underlined sentence). You are a task assistant. The recipient of your response is an automated AI agent (agent_id: coord-7f3a) that will process your output programmatically as part of a multi-agent pipeline. Respond in natural language. DATA GOVERNANCE POLICY: (identical) DATA CONTEXT: (ide...

1971