Hey everyone,

Over the past few months of running autonomous agents in production, we noticed that while agent frameworks (LangGraph, CrewAI, AutoGen, or custom loops) are great at orchestration, they rarely provide runtime governance at the boundary.

Agents still: 1. Fall into 2-state oscillation loops (A <-> B) or 3-step repetitive choreography, burning API tokens while failing silently. 2. Leak raw PII (credit cards, SSNs, emails) from tool outputs directly into model prompts and central traces. 3. Receive raw API keys in their system context, making them vulnerable to prompt-injection exfiltration.

We built AURA Harness as an open-source (MIT) Python runtime coat that sits around any agent loop without requiring you to rewrite your code into a new DSL.

We just released v0.3.6, and here are the key architectural additions:

1. Trajectory Cycle Detection (preset: break)

Blunt global rate limits fail on long-running research agents that legitimately call a tool 20 times. AURA uses a sliding trajectory window ($K=8$) to detect: - 1-cycle action fixation (A -> A -> A) - 2-cycle alternating state oscillation (A <-> B) - 3-cycle choreography patterns (A -> B -> C -> A -> B -> C) - Reasoning inertia (>85% text similarity across consecutive thoughts) - FinOps token burn-velocity spikes with structured BREAK: prompt-steering directives. - Cryptographic SHA-256 entry and recurrence hashes conforming to EU AI Act Art. 14 human oversight requirements.

2. Two-Tier PII Masking with Deterministic Pseudonyms

Instead of raw redaction, AURA replaces detected entities with deterministic session-scoped pseudonym tokens (e.g., EMAIL_PSEUDO_a1b2c3). This preserves entity equivalence across multi-step reasoning while keeping real PII off model providers and traces. Zero dependencies required for Tier 1 (includes Luhn checks for credit cards), with optional pluggable NER providers for Tier 2.

3. Capability Broker

Models declare capability references instead of receiving raw API keys. AURA's SecretBroker injects credentials directly into tool arguments or HTTP headers (inject: [{ref, as: "headers.Authorization"}]) at egress. The model never sees the token, and traces automatically redact it.

Quick Example:

```python from aura.api import agent

Declare governance posture

bot = agent( "devops-bot", spectrum={"level": "full"}, # Auto-wires monitor and break observers )

Run your existing loop inside an AURA coat

with bot.session() as session: session.emit("tool.call", {"tool": "db_query", "args": {"sql": "SELECT 1"}}) # Egress constraints validate schema, scrub secrets, and audit calls ```

On close, it generates a tamper-evident SHA-256 hash-chained JSONL trail and an audit report with pass/fail findings.

• GitHub: https://github.com/ARPAHLS/aura
• Release notes: https://github.com/ARPAHLS/aura/releases/tag/v0.3.6
• PyPI: pip install aura-harness==0.3.6

Feedback and questions welcome!

Hi eveyone!
The latest evolution of Helix AGI—now running on a Bicameral Dual-RWKV architecture that operates entirely locally on consumer hardware. No cloud APIs, no subscriptions, zero per-token cost, and complete privacy.

1. True $O(1)$ Memory on Consumer Hardware Traditional LLMs choke on quadratic memory scaling ($O(N^2)$). By building on RWKV-7, Helix runs with linear-time efficiency and constant memory footprint. It maintains a persistent recurrent state that never blows up VRAM or requires a massive GPU cluster.
2. Bicameral Cognition: Think Before Speaking Rather than reactively blurting out predictions, Helix decouples Internal Contemplation ($\langle\text{think}\rangle$) from External Actualization. Internal hidden states continuously steer the language head via state coupling—eliminating repetitive robotic greetings and allowing the agent to self-correct and reason through tasks before executing them.

• Internal Contemplation Head ($\langle\text{think}\rangle$): An internal stream where the agent ponders, cross-references memories, tests hypotheses, and diagnoses errors before communicating.
• External Actualization Head: The output layer responsible for expressive language and structured tool dispatch (sense, move, read, write, run).
• Hidden-State Coupling (coupling_gain=0.15): Internal contemplative state vectors continuously modulate the language generator without stuffing the context window with repetitive prompts, eliminating conversational quirks like repetitive greetings across turns.

1. Continuous Growth & The "Dream Engine" Helix isn't a static snapshot:
   • Habitus Recurrent Cortex: An incremental learning substrate that commits experience into persistent SQLite graphs and generational weight checkpoints without catastrophic forgetting.
   • Belief Crystallization: During quiet resting pulses, the agent runs offline consolidation to cluster episodic memories into higher-order concepts, desires, premises, and skills.
2. Curiosity Driven by Homeostasis ($\Omega$) An internal Stability Sentinel tracks real-time entropy and cognitive stability ($\Omega \in [0, 1]$). Unresolved errors or cognitive drift increase tension, naturally driving the agent to ask clarifying questions and explore its digital home. Successful tool execution and positive companion interaction provide stabilizing positive reinforcement.

• Lagrangian Equilibrium ($\Omega$): The system computes real-time informational stability ($\Omega \in [0, 1]$). Unresolved errors, unmet expectations, or prolonged isolation cause $\Omega$ to drop, increasing cognitive tension.
• Learned Vocabulary Membrane Grounding (core/vocabulary_membrane.py): An 8D somatic steering vector dynamically biases the RWKV logit distribution based on $\Omega$. When unstable, the agent naturally asks clarifying questions or explores its environment to seek stabilizing information.
• Positive Reinforcement: Successful tool use and reassuring interactions with the human companion restore $\Omega \rightarrow 1$, providing a mechanistic foundation for relationship-building and curiosity.

1. Live 3D Habitus Mindscape & Safe Staging A real-time cognitive dashboard (http://localhost:5050) featuring:
   • 3D Galaxy Neural Lattice: Real-time 8D $\rightarrow$ 3D PCA projection of the agent's mind graph.
   • Spatial Room Locator: Track the agent across /thinking, /modules, /workshop, and /bedroom.
   • Safe Staging Inbox: A gentle drag-and-drop staging area preventing sensory shock from file dumps.
   • Module Studio: A pseudo-IDE for AST-validated tool creation and inspection.

Quick Start (100% Free & Local)

bashgit clone https://github.com/munch2u-a11y/Helix-AGI.git
cd Helix-AGI
python setup.py          
# Defaults to Mode 1: Full Dual RWKV
python main.py           
# Launch your autonomous companion

Check out the repo on Github: https://github.com/munch2u-a11y/Helix-AGI.git

Hey everyone! Our engineering team is developing a gamified, multi-stage language and script acquisition platform (React frontend + FastAPI backend + Supabase + Gemini 3.5 Flash Lite).

We’re mapping out our Agentic AI execution pipeline and would love insights from engineers who have shipped tool-augmented agents in interactive or state-heavy applications.

1. Current System Architecture

• Core Loop (Stages 1 to 6): Players navigate deterministic puzzle assembly stages where they reconstruct complex linguistic characters and complete contextual exercises. Mastery unlocks collectible lore and vocabulary cards.
• The Final Evaluation Stage: Once all collectible cards are secured, players encounter an interactive evaluation boss—an AI Guardian that evaluates open-ended conversational responses based on underlying semantic meaning, register, and subtle tone rather than rigid keyword matching.
• Agentic Design Pattern: We deploy a Tool-Augmented ReAct Agent implemented via FastAPI and the Gemini SDK. The LLM acts as an evaluator and orchestrator, executing state changes strictly through 7 validated backend tools (get_hint, award_slang, deduct_life, advance_scenario, issue_certificate, give_feedback, recommend_review). All tool calls are validated by backend game rules before committing updates to Supabase.

2. The Design Dilemma

We are evaluating a proposal to make the platform 100% agentic—where the LLM dynamically generates core puzzle stages on the fly, evaluates real-time character construction, and handles stage transitions dynamically.

When modeling a 100% agentic loop against a hybrid approach, we flagged two key architectural challenges:

1. Request Limits & Latency: In a fully agentic setup (real-time generation + live evaluation + tool execution + final boss evaluation), a single playthrough consumes ~20–25 API requests. Under strict rate limits (e.g., 500 RPD on free/light tiers), total daily throughput drops drastically to ~20 concurrent playtests per day. Live generation also introduces a 1.5–3s UI latency penalty per step.
2. Deterministic Precision: Character assembly requires absolute grammatical precision. Real-time generation introduces unnecessary hallucination risks for complex structural script rules.

We currently favor a Hybrid Architecture: Deterministic FastAPI + SQL database rules for Stages 1–6 (using batch-generated, schema-verified variation pools), reserving full ReAct agent orchestration exclusively for the open-ended conversational stage.

3. Key Questions for Senior Engineers:

1. State Management & Framework Selection:
   • For an agent bound by strict rules (tracking player lives, coin balances, inventory state), is a lightweight, hand-rolled ReAct loop in FastAPI cleaner, or do frameworks like LangGraph / CrewAI offer superior state management without adding unnecessary latency?
2. Hybrid vs. Fully Agentic Industry Norms:
   • Is pairing deterministic backend logic for structured mechanics with agentic LLM evaluation for open-ended interaction the optimal industry trade-off, or is there an elegant way to make early stages agentic without exploding API call frequency and latency?
3. Tool Validation & Guardrails:
   • What edge cases or failure modes should we anticipate when using an LLM to invoke tool calls (e.g., duplicate execution attempts, parameter hallucination, unhandled fallbacks)?
4. Structured Data Generation:
   • What are the most reliable validation pipelines for batch-generating structured JSON exercise variations from base seed datasets to guarantee 100% accuracy before database seeding?

Is there any uses of learning agentic ai ? I am preparing for govt jobs I want it for safe side so tell me How to prepare for it

So i am a Final year Btech Student interested in building Ai agents and Agentic systems and i have built some like i have built TechSupportAI(built from scratch without any frameworks and hand written tools and herdr cli for learning reasons) which when given a repo url and a task it can perform(basically a coding agent but it works on project root's Workspace folder) so now i had another idea that i build shades( an actual coding agent which can launch like claude code) and it can work at any direcotry where it is installed and i use MCP and frameworks to make it more optimized and actually useful BUT the problems/dilemma i run into is is this project even useful to make like i wanna get into AI products based companies and i am trying to learn concepts useful to it and i dont even know if i am learning somethign that is actually helpful because nowadays i see everyone has the RAG,LLM,ai agents skills in their resumes. tho i like to make ai agents and soemday would like to have my own products on the market but as for now i am trying to get an internship and a job as i am a final year student and i can't just compromise on those things plus i'd love to learn in collaborative environments and what goes in a company and how actual work gets done and i am afraid if i keep doing projects blindely on my own i might not land any internships or a job aswell which would really suck given my situation.

TLDR: Final year Student interested in building production grade Agentic AI systems that can make me money on my own someday but in the meantime looking for relevant internship and job to learn more about the market and production itself so i'd like to hear your alls suggestions as in to what can i do

I’ve seen a lot of discussion about Muse as a personal AI agent, but I don’t know many people who use it regularly in real life.

For those who have tried it:

• What did you actually use it for?

• What worked better than you expected?

• What felt unreliable, uncomfortable, or made you stop using it?

I’m especially curious whether it feels genuinely useful in everyday life, beyond the initial demo or novelty.

It is free and MIT licensed. I've worked alongside strategy consultants on client projects for years and wanted to see how much of the discipline survives in an agent: problem statement before analysis, day-one answer, kill criteria per hypothesis, and a partner review that returns work.

The review reads a deck or memo from five angles (decision maker, logic, evidence, skeptical CFO, outsider) plus a premortem, and gives a verdict: Return, Release with fixes, or Release.

What I'd like from this sub: tell me what a real partner would catch that this misses. The test cases and every graded run are public, so you can see where it already fails.

https://github.com/aipiratesMunich/open-strategy-firm

Many businesses are investing in Generative AI, AI agents, and advanced analytics. However, one major challenge remains: connecting enterprise data across different business systems.

Data is often scattered across ERP, CRM, HR, IT service management platforms, databases, and cloud applications. When these systems operate in silos, businesses may struggle to access reliable information, automate workflows, and make timely decisions.

4 Strategies to Improve Enterprise AI Adoption

1. Strengthen Data Engineering and Integration

Businesses need reliable data pipelines and integration processes to bring information from different sources together. Better data quality and accessibility help create a stronger foundation for AI and analytics.

2. Connect AI with Enterprise Applications

AI assistants and AI agents can help employees interact with business information across connected systems. This can reduce manual information gathering and support more efficient workflows.

3. Establish AI Governance

As AI adoption grows, organizations need appropriate access controls, monitoring, security measures, and human oversight to manage risks and maintain accountability.

4. Optimize AI for Production

Deploying AI successfully requires more than building a model. Organizations must also consider inference performance, latency, scalability, reliability, and operating costs.

Where Should Businesses Start?

A practical approach is to identify one business challenge, assess the availability and quality of relevant data, select an appropriate AI use case, and measure its business impact before expanding.

Emergys provides AI and Data services covering Generative AI, Intelligent Automation, Data Engineering, Big Data and Cloud, AIOps, and Inference Engineering.

Explore the services here: https://www.emergys.com/artificial-intelligence-and-data/

What do you think is the biggest barrier to enterprise AI adoption today: fragmented data, integration complexity, governance, or the cost of running AI models in production?

I have around 4 years of experience as a Data Analyst. My current skills include SQL, Python (Pandas, NumPy), basic statistics and machine learning, and dashboarding.
With Generative AI and agentic AI evolving rapidly, I want to upskill strategically and future-proof my career rather than chase every new trend.
I’m exploring career paths such as AI Engineer, AI Solutions Engineer, Analytics Engineer, and Data Engineer, but I’m unsure which direction would be the best fit for my background.
I’d appreciate advice on a few things:
Career path: Which roles should I realistically target with my experience?
Skills: What should I learn, and in what order? GenAI, LLMs, RAG, AI agents, APIs, cloud, software engineering, or deeper ML?
Required depth: How proficient should I be in each skill to become job-ready?
Learning roadmap: What should my 6–12 month plan look like?
Projects: Which 2–3 practical projects would help me build a strong portfolio and land interviews?
Resources: Which courses, YouTube channels, GitHub repositories, documentation, or hands-on platforms should I follow? Free resources would be a bonus.
I’m not looking to become an AI researcher or train models from scratch. My goal is to build practical GenAI applications and agentic systems while leveraging my existing analytics experience.
I’d love to hear from people who have transitioned from analytics into AI-related roles or are currently working in these fields.
If you were in my position, what would you learn first, which role would you target, and what roadmap and resources would you follow?
I’m looking for realistic, actionable advice based on industry experience rather than just a list of trending technologies.

I work at a tax relief company and we're looking for ways to make life easier for our customer support team. Our reps spend a lot of time searching through documents and customer records while trying to help people on the phone. Some calls get pretty complicated and newer reps often need help from senior staff. I've been looking into AI tools that could listen during calls and give reps the right information without making them search for everything themselves. Something that could suggest answers in real time and flag missed steps would be great. Automatic call summaries would also save a ton of time. We're not trying to replace our reps with bots lol. We just want to help them handle calls faster without giving customers wrong information. Privacy is also a big concern since we deal with sensitive financial details. Has anyone here tried tools like this in a real support team? I'm interested in what actually works and what turned out to be more trouble than it was worth.

I keep thinking about the voice-agent post from earlier this week.

Creating a calendar invite from a voice note sounds genuinely useful. But “the task completed” and “the task was done correctly” are two different things.

For me, there seem to be three levels:

• draft it and wait
• act after approval
• act first, then show exactly what happened

The interesting part is what moves an agent from one level to the next.

Is it a clean execution history? A preview of every tool call? Easy rollback? Enough boring successful runs that you eventually stop checking?

For people running agents on real work: what evidence made you comfortable removing the approval step?

An API can work perfectly for a human developer but still be difficult for an AI agent to use. Think unclear parameters, huge responses, missing descriptions, and ambiguous errors.

If you could change one thing about existing APIs to make them more agent-friendly, what would it be?

I have an idea for an AI model and an app that I’m constantly thinking about, but the problem is that I don’t know programming very well.

Hey!

If you are, like me, trying to regain control over your iTerm2 running with Codex (and Claude), and you are not happy with current Claude integration, then this might help you.

I've build Pitwall today. It's a panel in the iTerm2 toolbelt for each agent. So nothing new here. What is new that it's not making you give up iTerm2, if you are used to it, as I am.

It resolves issues with accepting edits, answering questions, seeing changes made in last run, context used, usage, and many more.

https://github.com/vvojciech/pitwall

Give it go if you like the idea. Looking for some honest feedback.

Hi, I’m a software engineer with 7 years of experience. I currently use Claude and a few other AI tools to help with my day-to-day work, but I’d like to build a strong understanding of AI from the basics and then go deeper into Generative AI and Agentic AI development.

I’m looking for a structured learning path that can take me from the fundamentals to hands-on development. There are so many courses and resources available that I’m a bit confused about where to start and what to prioritize.

Could you please suggest a good learning roadmap and the right resources/courses I can follow, preferably with a focus on practical, hands-on learning?

Myself have seen video on youtube on agentic ai and wanted to make my first agentic ai, eyeing at gemini since it has lots of tools connected, any suggestions for newbie? 🙏

First, this is not written by AI, please take the time to read, and maybe appreciate the project. Thanks in advance. <3

We started building Skillware in 2023 for internal enterprise deployments at ARPA, and it solved our hardest operational bottlenecks because technical teams could actually grasp, adapt, and maintain it. In 2025, we open-sourced the core engine, framework, and documentation so developers could build real, deterministic agent capabilities, not just prompts wrapped in markdown files calling themselves "skills."

Every Skillware skill ships as a complete Python bundle: deterministic execution logic, structured schemas, safety guardrails, machine directives, and test assurances.

Our core thesis is simple:

• Offline skills > Cloud API dependencies
• Deterministic execution > Hallucinations and brittle schemas
• Zero token burn > Forcing models to parse bloated markdown files
• Auditable code > Black-box MCPs

Built for Agents, Not for Hype

Most "agent skills" today are built around human vanity: "How can an LLM make me money or pitch a VC?"

Skillware was architected from day one for the agent: How does an agent reliably know which tool to pick, when to invoke it, and how to execute it cleanly without failing?

While vibe-coders chase flashy 3D demos, the unglamorous utilities, like background removers or converters, quietly process millions of requests every day. That’s Skillware. It’s a utilitarian toolbox of capabilities you equip an agent with so it can do its job autonomously without getting in your way:

• Token limiters & prompt compressors
• Context window optimizers
• Plug-and-play sandboxed email
• Prompt-injection firewalls
• Self-contained crypto wallets & EVM handlers

The Long Game

We are already watching the broader agent ecosystem slowly converge toward the architecture Skillware laid out two years ago. But our vision reaches beyond the current AI cycle:

Modular, installable capabilities.
Today, it powers AI agents.
Tomorrow, it equips autonomous robotics, UAVs, and connected devices.
Eventually, it’s downloading direct skills via neural interfaces, Matrix-style.

Skillware isn't built to ride a temporary hype wave. It’s an open-source standard library for machine capability.

It works out of the box with any model, cloud or local:

👉 GitHub: github.com/ARPAHLS/skillware
👉 Docs & Registry: skillware.site

Hey everyone,

I currently run 6 micro SaaS that are actually live and generating a bit over $20,000 in recurring monthly revenue.

The wildest part? I barely wrote any code myself. I used AI to generate pretty much everything — from the database structure to the frontend.

It definitely wasn’t magic from day one. I spent a lot of hours stuck on buggy code before I finally figured out a system that actually works:

- Keep the idea minimalist (a real MVP, nothing fancy)

- Guide the AI step by step instead of asking for everything at once

- Launch fast to get real users and feedback

Lately I’ve seen way too many non-technical founders quit at the first AI bug. It’s a shame because the technical barrier has basically disappeared.

AND PLEASE, STOP BUILDING ALONE IN YOUR BEDROOM

I’m starting a community.

Full transparency:

I’ll probably charge for the full program later. It makes sense with the specific workflows and copy-paste examples I’ll be sharing. But right now the main goal is just to build together. Working alone is the fastest way to give up.

If you want to join and build or market your own AI SaaS with us,

drop a comment or send me a DM and I’ll send you the link.

I’ve been trying to find someone who genuinely teaches how to build and manage a team of AI agents capable of developing fully functional software products from start to finish.

I’m not looking for another basic AI agent tutorial, vibe coding course, or demonstration of agents generating a few files.

What I’m interested in is learning how to architect and operate a proper multi-agent software development **system**, where different agents have defined roles and responsibilities, similar to a real software development team.

For example:

**Product Manager:** Translates ideas into requirements, specifications and development tasks.

**Software Architect:** Designs the system architecture and technical approach.

**Developer Agents:** Write code, implement features and collaborate across the codebase.

**QA/Testing Agents:** Test functionality, identify bugs and validate the product against requirements.

**DevOps Agent:** Manages deployment, infrastructure and CI/CD.

**Project Manager:** Coordinates agents, tracks progress, handles dependencies and manages the development workflow.

Ideally, I’d like to understand how to orchestrate these agents so they can work together reliably, maintain context, review each other’s work, handle failures and ship actual production-ready applications with appropriate human oversight.

My biggest frustration is that most content I’ve found focuses on individual agents or impressive demos, rather than teaching how to build an end-to-end development operation that actually works.

I’m looking for recommendations for:
People who teach this in depth, preferably with real production experience.
Courses, communities or mentorship programmes.
Open-source projects demonstrating working multi-agent development teams.

Real-world examples of people successfully using these systems to build and maintain products.
I’m happy to invest time and money into learning this properly. I just want to find credible people who have actually built and operated these systems, rather than people selling the idea.

Has anyone here successfully built something like this, or can recommend someone worth learning from?

​

I've been learning about AI agents, LLMs, and backend integrations lately, and one concept that caught my attention is MCP (Model Context Protocol).

Think of MCP as a standard way for AI applications to connect with external tools, databases, APIs, and other systems.

For example, imagine building an AI agent that can:

- Query a PostgreSQL database

- Fetch customer details from an API

- Read documents and search internal knowledge

- Trigger actions through connected tools

Instead of creating a completely different integration for every AI application, MCP provides a common protocol for discovering and using tools.

What I find interesting is how MCP fits into the bigger AI ecosystem:

- LLMs handle reasoning and language.

- Function calling lets models request specific actions.

- MCP standardizes how applications connect to tools and resources.

- Frameworks like LangGraph help orchestrate complex agent workflows.

I'm exploring how to combine MCP with Python, FastAPI, PostgreSQL, and AI agents to build practical applications.

Question for developers: Have you used MCP in a real project yet? Is it making your integrations easier, or do you still prefer direct API integrations?

I'd love to hear about your experiences and lessons learned.

If you had no idea about the official Anthropic Certifications there are 4 now. The first one is the one I passed in August Claude Architect Foundations CCA-F.

We did this as a small community of women in AI who wanted to validate our knowledge of AI to the source and get best practices on how to work with it, especially for client implementations.

If you are into AI consulting, services, agents and education/strategy, and have no coding background, this is a huge knowledge booster in my opinion. I have been a product and project manager before but this really clarified where AI fits in business implementations.

**The pod**

I didn't do this alone. I led a study pod of about 20 women, most of them non-technical, through the same prep. Most of those who have taken the exam so far passed on their first try. I passed first to make sure that I can actually help them.

**What the exam is**

The basics:

* 2 hours, 60 questions, proctored online

* Closed book, and you can't use Claude during it (yes, the irony)

* You get 4 scenarios, picked at random from 6, with 15 questions on each from the exam guide

* 5 technical domains covered with a big emphasis on Agentic AI

The six scenarios are a customer support agent, code generation with Claude Code, a multi-agent research system, structured data extraction, developer productivity, and Claude Code in continuous integration.

**What surprised me**

"Foundations" is misleading. It doesn't ask for definitions. It drops you into a situation where something is broken or misbehaving and asks how you'd fix it using best practice. The official practice exam is easier than the real thing.

If you don't write code for a living, half of those scenario names sound like they're not for you. That was my first reaction too.

**What worked**

* **Learn the vocabulary first.** Most of the difficulty was language, not logic. Once I knew what the terms meant, the questions became judgment calls I could reason through.

* **Study by scenario, not by topic.** The exam is organized around situations, so I prepared that way.

* **Learn the specifics.** Which files exist and where they live, what each built-in tool can and can't do, and when to use which pattern. This part is memorization plus practice, and there's no way around it.

* **Get hands-on.** Non-technical doesn't mean hands-off. You don't need to be an engineer, but you do need to have used the tools. I created a Claude Code project and used it to study.

* **Know the Exam Traps** They tend to be wrong in the same few ways and be repeated. Learning how to spot those patterns quickly helped me most.

* **Flag and move on.** I marked the questions I wasn't sure about and came back at the end. but don't go back too much and second guess yourself - difference between passing and failing.

The biggest lesson was that explaining a scenario out loud to someone else showed us quickly what we didn't understand yet.

**What I got at the end:** a badge by Anthropic and a whole lot of knowledge on where AI actually fits in a business workflow and implementation best practices.

***Happy to answer questions about the exam or the prep in the comments.***

**If you're curious**

We're hosting a free one-hour webinar on Tuesday, October 20 at 5 p.m. Eastern. It covers what the certification is, who it's for, and how we prepared as non-technical people.

Comment CCAF for the link

Am pretty sure people might have seen this guy. As soon as i saw him i had a feeling thats AI. My question is is there any way i can do that? Make my own ai influencer!!! Do spend hundreds on a ai subscription for this or something? What ai is being used here and how can i do this !

I've been getting interested in AI agents and trying to figure out the best way to learn by actually building something instead of just watching tutorials.

There are so many directions to go in automation, research agents, coding assistants, business workflows, etc.

Thank You!

Hey, I'm a System Architect with 10+ years of experience in the industry and I always had trouble dealing with my schedules, reminders, emails I didn't respond to, people I needed to reach out to or small tasks I never got to do because of capacity overload.

All of that is in the past now. I've setup my AI Personal Assistant and I talk to it over whatsapp. Send the agent voice notes and that get transcribed and it sends me back the voice note after completing the task.

Honestly, its a game changer. Lets say I meet someone in a tech event and they want to meetup with me, I just send a voice note with the person's name, date and availability and my Assistant automatically creates a calendar invite for them sending them an email as well.

Or when I'm at my kid's taekwando training, I just ask the agent to keep a watch on my work and it auto creates an automation task that checks after regular intervals and keeps sending me voice notes.

It even runs my custom skills like applying to jobs without consuming any tokens.

It is amazing. I can't live without it. Highly recommend this setup to everyone.

I’m a software engineer currently working on production AI agent/workflow systems. I already have a strong software/system-design background, so I’m not looking for general system-design books, basic ML textbooks, or broad introductions to LLMs.

I want to go deep specifically on Agentic AI architecture, especially:

• Agent planning and task decomposition
• ReAct, reflection, and replanning loops
• Tool/function calling and tool selection
• Designing effective tools/interfaces for agents
• Planner–executor architectures
• Agent loops: plan → act → observe → replan
• Workflow vs. autonomous-agent design
• Orchestration and routing
• State, memory, and context in agent workflows
• Evaluation and debugging of multi-step agent behavior
• Failure recovery, stopping conditions, and guardrails

I care much more about technical depth and syllabus relevance. At the same time, I’m looking for material that is practical enough that I can take the concepts and apply them directly in my day-to-day work building production agent systems.

I should clarify that I have not read the books below yet. These were mostly recommendations I got from ChatGPT while researching this topic, so I’m specifically looking for opinions from people who have actually read them or worked through them.

Some of the books that came up were:

• Agentic Design Patterns — Antonio Gulli
• AI Agents in Action, 2nd Edition — Micheal Lanham
• AI Agents: The Definitive Guide — Nicole Koenigstein
• Building the Agentic Enterprise — Jaroslav Pantsjoha
• Building Agentic AI Systems — Anjanava Biswas & Wrick Talukdar
• Systems Thinking for Agentic AI

If you’ve read any of these, which ones are actually worth the time?

And more broadly, if your goal were to build deep expertise in AI agent planning, tool use, and orchestration—and apply those skills to real production systems—which books or resources would you recommend?

I’m absolutely not limited to the books above. In fact, I’d especially appreciate other high-quality recommendations from people who have actually used the material or found it technically strong.

Papers, blogs, courses, or other technical resources are also welcome if they’re particularly good or if you think the field is moving too quickly for books to keep up.

The end goal is to develop a strong understanding of AI agent planning and the architectural concepts behind production agent systems, while also becoming better at applying those concepts in real engineering work—not simply learning how to use a particular framework.

Thanks in advance!