👋 Welcome to Agents Work!
The agentseffect.com channel for product teams, researchers, leaders, and specialists bringing AI into real workflows.
Here we show how AI agents research audiences, shape strong solutions, review products, and transform working materials into verifiable artifacts.
What you will find:
• practical automation workflows;
• agent and subagent use cases;
• research, invention, and review methods;
• local, team, and cloud deployment options;
• implementation checklists.
Every workflow starts with a clear goal, approved sources, and acceptance criteria. A human confirms key handoffs and makes the final decision.
Choose a task and meet the right agent for your workflow 🤖
The agentseffect.com channel for product teams, researchers, leaders, and specialists bringing AI into real workflows.
Here we show how AI agents research audiences, shape strong solutions, review products, and transform working materials into verifiable artifacts.
What you will find:
• practical automation workflows;
• agent and subagent use cases;
• research, invention, and review methods;
• local, team, and cloud deployment options;
• implementation checklists.
Every workflow starts with a clear goal, approved sources, and acceptance criteria. A human confirms key handoffs and makes the final decision.
Choose a task and meet the right agent for your workflow 🤖
👍1
A chatbot supports a conversation, while an AI agent organizes work around a defined goal: it receives inputs, follows explicit stages, and produces a verifiable result. Understanding this distinction helps teams choose a tool for the actual process rather than for its label.
The full article covers five selection criteria: goal, stages, tools, output format, and human control.
Inside the article:
• The Main Difference: The Form of Responsibility
• The passport matters more than the model name
• Autonomy as a Range
• What to choose for your task
Practical focus: A practical comparison based on observable responsibilities.
In interfaces the word 'agent' is often used for everything — from a prompt to an autonomous system.
A chatbot can give a very good answer and even call a tool.
An agent system describes these duties in advance and makes them a visible part of the interface.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article covers five selection criteria: goal, stages, tools, output format, and human control.
Inside the article:
• The Main Difference: The Form of Responsibility
• The passport matters more than the model name
• Autonomy as a Range
• What to choose for your task
Practical focus: A practical comparison based on observable responsibilities.
In interfaces the word 'agent' is often used for everything — from a prompt to an autonomous system.
A chatbot can give a very good answer and even call a tool.
An agent system describes these duties in advance and makes them a visible part of the interface.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
❤1
Google Cloud Blog
State of AI infrastructure report agent governance and security | Google Cloud Blog
To be useful and secure, AI agents need access — and also guardrails.
Security governance is becoming core infrastructure for autonomous agents
Google Cloud examines how AI agents should access email, databases, and APIs while keeping every action governed and observable. The article connects agent permissions with the practical requirements of enterprise deployment.
Why this is interesting: in my view, the important shift is from discussing model capability to designing authority for a specific digital operator. An agent becomes more useful when its context, permissions, and action journal are designed as one system.
Read the Google Cloud article
#AIAgents #AISecurity #AIGovernance #AgenticAI
Google Cloud examines how AI agents should access email, databases, and APIs while keeping every action governed and observable. The article connects agent permissions with the practical requirements of enterprise deployment.
Why this is interesting: in my view, the important shift is from discussing model capability to designing authority for a specific digital operator. An agent becomes more useful when its context, permissions, and action journal are designed as one system.
Read the Google Cloud article
#AIAgents #AISecurity #AIGovernance #AgenticAI
👍1
Agents Work | AI Agents, Automation, Workflows pinned «👋 Welcome to Agents Work! The agentseffect.com channel for product teams, researchers, leaders, and specialists bringing AI into real workflows. Here we show how AI agents research audiences, shape strong solutions, review products, and transform working…»
The value of an AI agent depends less on a job title than on a repeatable process and a clearly defined result. Executives need metrics with calculations, experts need source references, product teams need working artifacts, and operations teams need a reliable data-processing route.
The full article examines four roles and the result each can gain.
Inside the article:
• The Selection Criterion: The Process
• The Executive: Metrics on Demand
• The expert: lawyer, analyst, engineer
• The product team: artifacts instead of chat
Practical focus: A useful starting question is: "Which process repeats and becomes difficult to scale?" The signs of such a process: typical inputs, a stable result format, the need to check the output and a queue of people waiting for the material.
If the process is one-off — no agent is needed.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article examines four roles and the result each can gain.
Inside the article:
• The Selection Criterion: The Process
• The Executive: Metrics on Demand
• The expert: lawyer, analyst, engineer
• The product team: artifacts instead of chat
Practical focus: A useful starting question is: "Which process repeats and becomes difficult to scale?" The signs of such a process: typical inputs, a stable result format, the need to check the output and a queue of people waiting for the material.
If the process is one-off — no agent is needed.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
A polished demo shows an interface. A sound selection starts with questions about data, permissions, sources, approval points, and the final artifact. These parameters reveal how an agent will operate in a real workflow.
The full article provides a practical seven-question checklist for comparing solutions.
Inside the article:
• Seven Questions from the Agent Passport
• Questions 1–3: task, data, stopping
• Questions 4–5: sources and the boundaries of claims
• Questions 6–7: environment and artifact
Practical focus: These seven questions come directly from the eight fields of the agent passport defined for every launch: task, input, process, output, limits, permissions, environment and verification (more on the pricing page).
An agent with a completed passport answers every question; an agent without a passport is a polished presentation with unpredictable behavior.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article provides a practical seven-question checklist for comparing solutions.
Inside the article:
• Seven Questions from the Agent Passport
• Questions 1–3: task, data, stopping
• Questions 4–5: sources and the boundaries of claims
• Questions 6–7: environment and artifact
Practical focus: These seven questions come directly from the eight fields of the agent passport defined for every launch: task, input, process, output, limits, permissions, environment and verification (more on the pricing page).
An agent with a completed passport answers every question; an agent without a passport is a polished presentation with unpredictable behavior.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
huggingface.co
How Much Memory Does Your Agent Actually Need?
A Blog post by IBM Research on Hugging Face
How much memory does an AI agent actually need?
IBM Research shows that agent memory should be matched to the model. Selective memory delivered gains of up to 16.1 percentage points with only a 5% increase in token usage.
Why this is interesting: In my view, this is a useful reminder that more context does not automatically produce a better result. Memory should be a designed workflow capability whose value is measured alongside its cost.
Source: Hugging Face · IBM Research · 2026-08-18
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#AIAgents #AgenticAI #AIResearch #AgentsEffect
IBM Research shows that agent memory should be matched to the model. Selective memory delivered gains of up to 16.1 percentage points with only a 5% increase in token usage.
Why this is interesting: In my view, this is a useful reminder that more context does not automatically produce a better result. Memory should be a designed workflow capability whose value is measured alongside its cost.
Source: Hugging Face · IBM Research · 2026-08-18
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
MuleSoft Blog
Architecting the Agentic Enterprise: Unifying Context, Control and Activation for AI Agents
Every organisation wants to become AI-driven. Yet many are attempting to build AI capabilities on fragmented data, disconnected systems, inconsistent…
Architecting the agentic enterprise
MuleSoft identifies three pillars of implementation: trusted context, controlled actions, and measurable business outcomes. Together they form a foundation for scalable agentic systems.
Why this is interesting: In my view, these three pillars connect technology and governance particularly well. Context supports decision quality, controls define acceptable action, and metrics establish business value.
Source: MuleSoft · 2026-08-18
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#AIAgents #AgenticAI #AIResearch #AgentsEffect
MuleSoft identifies three pillars of implementation: trusted context, controlled actions, and measurable business outcomes. Together they form a foundation for scalable agentic systems.
Why this is interesting: In my view, these three pillars connect technology and governance particularly well. Context supports decision quality, controls define acceptable action, and metrics establish business value.
Source: MuleSoft · 2026-08-18
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
A strong first automation candidate has three qualities: it repeats regularly, uses understandable inputs, and ends with a verifiable result. This lets the team gain practical value quickly and assess quality at every stage.
The full article reviews five candidates: email, meetings, contracts, metrics, and specifications.
Inside the article:
• The criteria for the first process
• Candidate 1: incoming email
• Candidate 2: meeting minutes
• Candidate 3: search across contracts
Practical focus: Before choosing the first candidate, check four signs:
The same principles are defined in the engagement models: before launch the task, the input data, the expected artifact, the acceptance criteria, the limits and the person who makes the decision are defined.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article reviews five candidates: email, meetings, contracts, metrics, and specifications.
Inside the article:
• The criteria for the first process
• Candidate 1: incoming email
• Candidate 2: meeting minutes
• Candidate 3: search across contracts
Practical focus: Before choosing the first candidate, check four signs:
The same principles are defined in the engagement models: before launch the task, the input data, the expected artifact, the acceptance criteria, the limits and the person who makes the decision are defined.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
AI-agent implementation becomes manageable when the team defines the task, acceptance criteria, permissions, integrations, and support rules in advance. A clear sequence connects the technology to a concrete business result.
The full article explains five implementation stages and the work included in each.
Inside the article:
• Stage 1. Investigation
• Stage 2. Passport and permissions
• Stage 3. Installation and integration
• Stage 4. Launch with control
Practical focus: The task, input data, expected artifact, acceptance criteria, constraints, and decision-maker are defined.
The composition of the integrations, the environment and the cost are determined after the investigation — not before it.
The eight fields of the passport are documented: the task, the input, the process, the output, the limitations, the permissions, the environment and the verification.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article explains five implementation stages and the work included in each.
Inside the article:
• Stage 1. Investigation
• Stage 2. Passport and permissions
• Stage 3. Installation and integration
• Stage 4. Launch with control
Practical focus: The task, input data, expected artifact, acceptance criteria, constraints, and decision-maker are defined.
The composition of the integrations, the environment and the cost are determined after the investigation — not before it.
The eight fields of the passport are documented: the task, the input, the process, the output, the limitations, the permissions, the environment and the verification.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
AGI Hunt
AGI HUNT · AI 资讯日报 2026-08-20 — 今日 AI 热点总结与观察
过去 24 小时全站最热 AI 动态的结构化梳理:今日总结、与昨日对比、各频道观察与主要公司动态,每日 06:00(北京时间)自动生成。
The physics of communities with ten thousand AI agents
This Chinese review describes research on communities of 10,000 LLM agents. Researchers connect consensus, polarization, and correction of an initially wrong majority to an Ising-model interpretation.
Why this is interesting: In my view, experiments like this provide a vocabulary for measuring collective agent behavior. That matters when an error can be amplified through group interaction rather than remaining inside one agent.
Source: AGI HUNT · 2026-08-20
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
This Chinese review describes research on communities of 10,000 LLM agents. Researchers connect consensus, polarization, and correction of an initially wrong majority to an Ising-model interpretation.
Why this is interesting: In my view, experiments like this provide a vocabulary for measuring collective agent behavior. That matters when an error can be amplified through group interaction rather than remaining inside one agent.
Source: AGI HUNT · 2026-08-20
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
Salesforce
Salesforce Turns Enterprise Applications into Enterprise Capabilities
Headless 360 is expanding across the Salesforce platform, transforming every Salesforce cloud into reusable enterprise capabilities that any authorized AI
Enterprise functions are becoming reusable agent tools
Salesforce introduces more than 100 reusable Skills, MCP servers, shared permissions, metadata, and business logic. Enterprise capabilities are becoming modular tools for agentic workflows.
Why this is interesting: In my view, a reusable skill is one of the central building blocks of an agentic enterprise. It separates a validated business capability from any single interface or model.
Source: Salesforce · 2026-08-19
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
Salesforce introduces more than 100 reusable Skills, MCP servers, shared permissions, metadata, and business logic. Enterprise capabilities are becoming modular tools for agentic workflows.
Why this is interesting: In my view, a reusable skill is one of the central building blocks of an agentic enterprise. It separates a validated business capability from any single interface or model.
Source: Salesforce · 2026-08-19
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
An implementation budget depends on the scope of the function, the number of integrations, data requirements, and the final artifact. Offers are easier to compare by examining the launch scope and the expected effect on the process.
The full article covers three engagement models, what the price includes, and how to estimate payback.
Inside the article:
• Three engagement models
• What is included in the launch price
• How to estimate payback honestly
• Conditions for Implementation Payback
Practical focus: The pricing page presents three engagement models.
The choice depends on the task, the data and the infrastructure requirements.
The stated price covers the implementation work.
The client pays separately for any required infrastructure and subscriptions, with support available for selection and assessment.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article covers three engagement models, what the price includes, and how to estimate payback.
Inside the article:
• Three engagement models
• What is included in the launch price
• How to estimate payback honestly
• Conditions for Implementation Payback
Practical focus: The pricing page presents three engagement models.
The choice depends on the task, the data and the infrastructure requirements.
The stated price covers the implementation work.
The client pays separately for any required infrastructure and subscriptions, with support available for selection and assessment.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
An agent leads an end-to-end process to a completed artifact, a subagent performs one specialized function, and a human makes decisions at control points. Clear role distribution makes automation transparent and manageable.
The full article explains three levels of responsibility and how to choose the right structure.
Inside the article:
• Three levels of responsibility
• Where the boundary is visible in the product
• Why the boundary is set before launch
• How to choose the level
Practical focus: Access rights, sources, permitted actions, and the confirmation point are defined before the subagent or agent is connected.
This protects both the company and the result: the higher the autonomy and the consequences of a mistake, the stricter the permissions, the journaling and the human confirmation.
One useful function in a familiar process — a subagent.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article explains three levels of responsibility and how to choose the right structure.
Inside the article:
• Three levels of responsibility
• Where the boundary is visible in the product
• Why the boundary is set before launch
• How to choose the level
Practical focus: Access rights, sources, permitted actions, and the confirmation point are defined before the subagent or agent is connected.
This protects both the company and the result: the higher the autonomy and the consequences of a mistake, the stricter the permissions, the journaling and the human confirmation.
One useful function in a familiar process — a subagent.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
agenccy.ai
Пять LLM-шлюзов, пять провалов: исследователи считывают данные между клиентами через prompt cache
Исследование показало, что все пять протестированных open-source реле по умолчанию допускают межклиентские чтения из cache, а исправление стоит менее 2,5%.
Researchers test prompt-cache isolation in LLM gateways
A study of five open-source gateways found cross-client data exposure through prompt caches under default configurations. The authors also report that stronger isolation costs less than 2.5% in overhead.
Why this is interesting: In my view, the quantitative result is especially useful: meaningful isolation can be achieved at modest cost. For enterprise agents, cache data must be treated as protected context rather than a neutral optimization layer.
Source: AI News Today · RU · 2026-08-23
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#AIAgents #AgenticAI #AIResearch #AgentsEffect
A study of five open-source gateways found cross-client data exposure through prompt caches under default configurations. The authors also report that stronger isolation costs less than 2.5% in overhead.
Why this is interesting: In my view, the quantitative result is especially useful: meaningful isolation can be achieved at modest cost. For enterprise agents, cache data must be treated as protected context rather than a neutral optimization layer.
Source: AI News Today · RU · 2026-08-23
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
A work result becomes verifiable when the path from a conclusion back to the source data is visible. An agent journal records inputs, stages, decisions, sources, and human approvals.
The full article explains the journal structure and how traceability supports business work.
Inside the article:
• What the journal records
• Traceability in the products
• Why the business needs it
• The Journal and Expert Verification
Practical focus: The journal turns the result from a system-generated opinion into material with an evidence base: checking a conclusion takes minutes, responsibility for the decision is clear, a new employee sees how the artifact was obtained, and an audit gets a reproducible history of the launch.
The journal shows the path of the result but does not make it true: critical conclusions are confirmed by a human with the relevant expertise.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article explains the journal structure and how traceability supports business work.
Inside the article:
• What the journal records
• Traceability in the products
• Why the business needs it
• The Journal and Expert Verification
Practical focus: The journal turns the result from a system-generated opinion into material with an evidence base: checking a conclusion takes minutes, responsibility for the decision is clear, a new employee sees how the artifact was obtained, and an audit gets a reproducible history of the launch.
The journal shows the path of the result but does not make it true: critical conclusions are confirmed by a human with the relevant expertise.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
Thomson Reuters
Agentic AI and legal: How it’s redefining the profession
Agentic AI is here. Discover how it's transforming legal work, enabling lawyers to automate complex tasks while focusing on high-value client strategy.
Agentic AI is reshaping legal work
Thomson Reuters explores research, contract analysis, and due diligence with professional oversight. Agents accelerate discovery and preparation while specialists retain responsibility for validation and decisions.
Related Agents Effect product: Legal RAG Navigator.
Why this is interesting: In my view, legal work demonstrates a productive human-agent model particularly well. Citations, data provenance, and an explicit approval point become part of the deliverable itself. A practical counterpart: Legal RAG Navigator.
Source: Thomson Reuters · 2026-08-21
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#AIAgents #AgenticAI #AIResearch #AgentsEffect
Thomson Reuters explores research, contract analysis, and due diligence with professional oversight. Agents accelerate discovery and preparation while specialists retain responsibility for validation and decisions.
Related Agents Effect product: Legal RAG Navigator.
Why this is interesting: In my view, legal work demonstrates a productive human-agent model particularly well. Citations, data provenance, and an explicit approval point become part of the deliverable itself. A practical counterpart: Legal RAG Navigator.
Source: Thomson Reuters · 2026-08-21
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
ChannelPro
Fortinet eyes AI security gains with Virtue AI acquisition
The acquisition will add AI runtime protection and automated validation capabilities to Fortinet’s existing Security for AI portfolio
Fortinet expands its agentic AI security capabilities
The Virtue AI acquisition adds automated red teaming, MCP tool assessment, and runtime controls. Security is becoming an integrated part of the agent lifecycle.
Why this is interesting: In my view, the key shift is from one-time model testing to continuous oversight of the whole agent system. Tools, permissions, and actions must be evaluated together because their combination determines real behavior.
Source: ITPro · 2026-08-19
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#AIAgents #AgenticAI #AIResearch #AgentsEffect
The Virtue AI acquisition adds automated red teaming, MCP tool assessment, and runtime controls. Security is becoming an integrated part of the agent lifecycle.
Why this is interesting: In my view, the key shift is from one-time model testing to continuous oversight of the whole agent system. Tools, permissions, and actions must be evaluated together because their combination determines real behavior.
Source: ITPro · 2026-08-19
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect
Human control is a designed part of the workflow: the agent prepares a result, shows its evidence, and pauses before a consequential action. This approach preserves processing speed while keeping decisions with the accountable specialist.
The full article presents stop rules and examples of control points for different tasks.
Inside the article:
• The stop rule
• Where the control points stand
• Maintaining Pace with Control Points
• The Role of Control Points in the Process
Practical focus: The practical rule of the project: if an action is hard to undo, affects other people or uses sensitive data, implied consent is not enough.
That is why no stage passes silently — disputed decisions stop and wait for confirmation.
A human reviews prepared material instead of repeating the agent's work: a draft with its rationale, a set of quotes, or meeting minutes with uncertain points flagged.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
The full article presents stop rules and examples of control points for different tasks.
Inside the article:
• The stop rule
• Where the control points stand
• Maintaining Pace with Control Points
• The Role of Control Points in the Process
Practical focus: The practical rule of the project: if an action is hard to undo, affects other people or uses sensitive data, implied consent is not enough.
That is why no stage passes silently — disputed decisions stop and wait for confirmation.
A human reviews prepared material instead of repeating the agent's work: a draft with its rationale, a set of quotes, or meeting minutes with uncertain points flagged.
Read the full article at the link below.
#AIAgents #Automation #AIImplementation #AgentsEffect
https://agentseffect.com/
huggingface.co
Up to 3.2x Faster Inference with LFM2.5-DSpark
A Blog post by Liquid AI on Hugging Face
Faster local inference for agentic models
Liquid AI reports up to 3.2x faster inference and an average 57% reduction in function-calling latency. This is especially relevant for local and always-on agents.
Why this is interesting: In my view, tool-call latency directly shapes how useful an agent feels in practice. Faster local execution makes private-data workflows, rapid action loops, and predictable costs more realistic.
Source: Hugging Face · Liquid AI · 2026-08-20
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#AIAgents #AgenticAI #AIResearch #AgentsEffect
Liquid AI reports up to 3.2x faster inference and an average 57% reduction in function-calling latency. This is especially relevant for local and always-on agents.
Why this is interesting: In my view, tool-call latency directly shapes how useful an agent feels in practice. Faster local execution makes private-data workflows, rapid action loops, and predictable costs more realistic.
Source: Hugging Face · Liquid AI · 2026-08-20
Open the original
#AIAgents #AgenticAI #AIResearch #AgentsEffect