Self-evolving AI agents: from static LLMs to systems that learn on the job

 Agent frameworks are great at demo day, brittle in the wild. A sweeping new survey argues the fix isn’t a bigger model but a new self-evolving paradigm: agents that keep improving after deployment using the data and feedback their work naturally produces. The paper pulls scattered ideas under one roof and offers a playbook for researchers and startups building agents that won’t ossify after v1.0. 

The big idea: turn agents into closed-loop learners

The authors formalize a feedback loop with four moving parts—System Inputs, the Agent System, the Environment, and Optimisers—and show how different research threads plug into each stage. Think: collecting richer traces from real use (inputs), upgrading skills or tools (agent system), instrumenting the app surface (environment), and choosing the learning rule (optimisers). 

A working taxonomy you can implement

Within that loop, the survey maps techniques you can mix-and-match:

• Single-agent evolution: self-reflection, memory growth, tool discovery, skill libraries, meta-learning and planner refinements driven by interaction data.

• Multi-agent evolution: division-of-labour curricula, role negotiation, and team-level learning signals so collectives improve—not just individuals.

• Domain programs: recipes specialized for biomed, programming, and finance, where optimization targets and constraints are domain-specific. 

Evaluation and safety don’t lag behind

The paper argues for verifiable benchmarks (exact-match tasks, executable tests, grounded web tasks) so improvements aren’t just prompt luck. It also centers safety and ethics: guarding against reward hacking, data poisoning, distribution shift, and privacy leaks that can arise when models learn from their own usage. 

Why this matters now

• Static fine-tunes stagnate. Post-training once, shipping, and hoping for the best leaves quality on the table as tasks drift.

• Logs are learning fuel. Structured traces, success/failure signals, and user edits are free gradients if you design the loop.

• From demos to durable systems. The framework gives teams a shared language to plan what to learn, when, and how to verify it—before flipping the “autonomous improvement” switch. 

If you’re building an assistant, coder, or web agent you expect to live for months, this survey is a pragmatic roadmap to keep it getting better—safely—long after launch.

Paper link: arXiv 2508.07407 (PDF)

Oracle Will Offer Google’s Gemini Models via OCI—A Pragmatic Shortcut to Agentic AI at Enterprise Scale

Oracle and Google Cloud have expanded their partnership so Oracle customers can tap Google’s latest Gemini family directly from Oracle Cloud Infrastructure (OCI) and across Oracle’s business applications. Announced on August 14, 2025, the deal aims squarely at “agentic AI” use cases—bringing planning, tool use, and multimodal generation into day-to-day enterprise workflows. 

What’s new: Oracle says it will make “the entire range” of Google’s Gemini models available through OCI Generative AI, via new integrations with Vertex AI. That includes models specialized for text, image, video, speech and even music generation, with the initial rollout starting from Gemini 2.5. In other words, teams can compose end-to-end agents—retrieve data, reason over it, and produce rich outputs—without leaving Oracle’s cloud. 

Enterprise reach matters here. Beyond developer access in OCI, Oracle notes that customers of its finance, HR, and supply-chain applications will be able to infuse Gemini capabilities into daily processes—think automated close packages, job-description drafting, supplier-risk summaries, or multimodal incident explainers. The practical promise: fewer swivel-chair handoffs between tools and more AI-assisted outcomes where people already work. 

Buying and operating model: Reuters reports customers will be able to pay for Google’s AI tools using Oracle’s cloud credit system, preserving existing procurement and cost controls. That seemingly small detail removes a classic blocker (separate contracts and billing) and makes experimentation less painful for IT and finance. 

Why this partnership, and why now?

• For Oracle, it broadens choice. OCI already aggregates multiple model providers; adding Gemini gives customers a top-tier, multimodal option for agentic patterns without forcing a provider switch.
• For Google Cloud, it’s distribution. Gemini lands in front of Oracle’s substantial enterprise base, expanding Google’s AI footprint in accounts where the “system of record” lives in Oracle apps. 

What you can build first

• Multimodal service agents: ingest PDFs, images, and call transcripts from Oracle apps; draft actions and escalate with verifiable citations.
• Supply-chain copilots: analyze shipments, supplier news, and inventory images; generate risk memos with recommended mitigations.
• Finance and HR automations: summarize ledger anomalies, produce policy-compliant narratives, or generate job postings with skills mapping—then loop a human approver before commit. (All of these benefit from Gemini’s text, image, audio/video understanding and generation.) 

How it fits technically

The integration path leverages Vertex AI on Google Cloud as the model layer, surfaced to OCI Generative AI so Oracle developers and admins keep a single operational pane—policies, observability, and quotas—while calling Gemini under the hood. Expect standard SDK patterns, prompt templates, and agent frameworks to be published as the rollout matures. 

Caveats and open questions

Availability timing by region, specific pricing tiers, and which Gemini variants (e.g., long-context or domain-tuned models) will be enabled first weren’t fully detailed in the initial announcements. Regulated industries will also look for guidance on data residency and cross-cloud traffic flows as deployments move from pilots to production. For now, the “pay with Oracle credits” and “build inside OCI” signals are strong green lights for proofs of concept. 

The takeaway

By making Google’s Gemini models first-class citizens in OCI and Oracle’s application stack, both companies reduce friction for enterprises that want agentic AI without a multi-vendor integration slog. If your roadmap calls for multimodal assistants embedded in finance, HR, and supply chain—or developer teams building agents against Oracle data—this partnership lowers the barrier to getting real value fast. 

GLM-4.5 wants to be the open-source workhorse for agents, reasoning, and code

 Zhipu AI just dropped GLM-4.5, a Mixture-of-Experts LLM built to juggle three hard modes at once: agentic tasks, deep reasoning, and real-world coding. The headline specs: 355B total parameters with 32B active per token, a 23-trillion-token training run, and a hybrid reasoning switch that flips between “think-out-loud” and terse answers based on task demands. There’s also a slimmer GLM-4.5-Air (106B/12B active) for teams who can’t babysit a mega-model. 

Why it stands out

• ARC trifecta focus. Across 12 benchmarks, GLM-4.5 places #3 overall and #2 on agentic suites—with marquee scores like 91.0 on AIME’24, 64.2 on SWE-bench Verified, and 70.1 on TAU-Bench. It also reports 26.4 on BrowseComp for web agents, near OpenAI’s o4-mini-high in the authors’ runs. 

• Parameter-efficient MoE. Compared to some giant peers, GLM-4.5 keeps active params modest while stacking deeper layers, 96 attention heads, partial RoPE, QK-Norm, and a built-in MTP layer for speculative decoding. 

• Hybrid reasoning as a product feature. Both GLM-4.5 and Air support thinking (for complex tool use) and non-thinking (instant replies) modes from the same checkpoint. 

The training recipe (quick hits)

A two-stage pretraining + mid-training stack mixes high-quality web, multilingual, code, math/science, then adds repo-level code, synthetic reasoning, 128K-token long-context, and agent trajectories to push real software-engineering and planning skills. Post-training distills expert Reasoning, Agent, and General models into one hybrid generalist, followed by targeted RL (including a “pathology RL” cleanup pass). 

What you can actually download

Zhipu has published code, evals, and model cards on GitHub; weights are also listed on Hugging Face. The team pitches GLM-4.5 as agent-first and ships a simple eval harness to reproduce scores. 

Bottom line

Open-source has plenty of great single-skill models. GLM-4.5 is aiming for a different bullseye: one backbone that can browse, reason, and patch code without feeling second-tier. If the reported ARC numbers hold up in the wild, this could become the go-to open checkpoint for production-grade agents.

Paper link: arXiv 2508.06471 (PDF)

OpenAI Unveils GPT-OSS: Two Apache-Licensed Open-Weight Models Aimed at Reasoning, Agents, and Real-World Deployment

 OpenAI has released GPT-OSS, a pair of open-weight language models designed for strong reasoning and agentic workflows—gpt-oss-120b and gpt-oss-20b—marking the company’s most significant “open” move since GPT-2. Both models are distributed under Apache 2.0 (with an accompanying GPT-OSS usage policy), positioning them for commercial use, customization, and local deployment. 

What’s in the release

• Two sizes, one family. The larger gpt-oss-120b targets top-tier reasoning; gpt-oss-20b is a lighter option for edge and on-prem use. OpenAI says 120b achieves near-parity with o4-mini on core reasoning benchmarks, while 20b performs similarly to o3-mini—a notable claim for open-weight models. 

• Hardware footprint. OpenAI highlights efficient operation for the 120b model (single 80 GB GPU) and 20b running with as little as 16 GB memory in edge scenarios, enabling local inference and rapid iteration without costly infrastructure. 

• Licensing & model card. The company published a model card and licensing details (Apache 2.0 + usage policy), clarifying intended use, evaluations, and limitations. 

Why this matters

For years, OpenAI prioritized API-only access to frontier systems. GPT-OSS signals a strategic broadening toward open-weight distribution, meeting developers where they build—local, cloud, or hybrid—and competing more directly with leaders like Llama and DeepSeek. Early coverage underscores the shift: outlets note this is OpenAI’s first open-weight release since GPT-2 and frame it as both an ecosystem and competitive move. 

Where you can run it (day one)

OpenAI launched with unusually wide partner support, making GPT-OSS easy to try in existing MLOps stacks:

• Hugging Face: downloadable weights and a welcome post with implementation details. 

• AWS SageMaker JumpStart: curated deployment templates for OSS-20B/120B. 

• Azure AI Foundry & Windows AI Foundry: managed endpoints and tooling for fine-tuning and inference. 

• Databricks: native availability with 131k-context serving options and enterprise controls. 

• NVIDIA: performance tuning for GB200 NVL72 systems; NVIDIA cites up to ~1.5M tokens/sec rack-scale throughput for the 120B variant. 

Developer ergonomics: Harmony & agents

OpenAI also published Harmony, a response format and prompt schema that GPT-OSS models are trained to follow. Harmony standardizes conversation structure, reasoning output, and function-calling/tool-use—useful for building agents that require predictable JSON and multi-step plans. If you’re serving via common runtimes (Hugging Face, vLLM, Ollama), the formatting is handled for you; custom servers can adopt the schema from the public repo. 

Safety posture

OpenAI says GPT-OSS went through Preparedness Framework testing, including trials where a maliciously fine-tuned 120B model was evaluated for risky capabilities. The company reports that such variants did not reach high-capability thresholds, presenting a measured step forward in open-model safety practices. 

How it stacks up (early read)

Early reports highlight the significance of the move and the headline performance claims—near-o4-mini for 120B and o3-mini-like results for 20B—alongside the practical win of local, customizable models under a permissive license. Analysts also point out the competitive context: GPT-OSS arrives as open-weight ecosystems (Llama, DeepSeek, Qwen, Kimi) surge in adoption. 

What to build first

• Agent backends that rely on structured tool use and local policy control (Harmony + Apache 2.0 helps here). 

• Sovereign/air-gapped deployments in regulated environments using on-prem GPUs or edge hardware, especially with the 20B model. 

• Cost-sensitive RAG and analytics where fine-tuning and local inference can beat per-token API economics—now supported across major clouds and MLOps platforms.  

The takeaway

GPT-OSS is OpenAI’s clearest embrace of the open-weight ecosystem to date: credible reasoning performance, permissive licensing, broad partner availability, and practical tooling for agents. If your roadmap calls for customizable, locally deployable models with strong reasoning, GPT-OSS belongs on your shortlist—whether you’re targeting laptops, single-GPU servers, or GB200-class scale.

ReaGAN turns every node into an agent—with a plan, memory, and tools

 Classical GNNs push messages with one global rule per layer—great for tidy graphs, brittle for messy ones. ReaGAN (Retrieval-augmented Graph Agentic Network) breaks that mold by treating each node as an autonomous agent that decides whether to aggregate locally, retrieve globally, predict now, or do nothing—based on its own memory and a plan drafted by a frozen LLM. 

What’s new

• Node-level autonomy. At every layer, a node queries the LLM for an action plan, executes it, and updates memory—no globally synchronized rulebook. 

• Local + global context. Beyond neighbors in the graph, nodes invoke RAG to retrieve semantically similar but structurally distant nodes, then fuse both sources. 

• Memory as glue. Nodes persist aggregated text snippets and few-shot (text, label) exemplars, enabling in-context prediction later. 

Why it matters

Real-world graphs are sparse and noisy; uniform propagation amplifies junk. ReaGAN’s per-node planning and local-global retrieval adapt to informativeness imbalances and long-range semantics—key gaps in standard GNNs. In experiments, the authors report competitive few-shot performance using only a frozen LLM (no fine-tuning), highlighting a compute-friendly path for graph ML. 

How it runs (at a glance)

Each node iterates a loop: perceive → plan → act (LocalAggregation / GlobalAggregation / Predict / NoOp) → update memory. A simple algorithmic skeleton formalizes the layer-wise cycle and action space. 

Paper link: https://arxiv.org/pdf/2508.00429

Building Startups at the Speed of AI: Key Takeaways from Andrew Ng’s Startup School Talk

 

1 Speed Is the Leading Indicator of Success

At AI Fund, Andrew Ng’s venture studio, teams launch roughly one startup a month. After hundreds of “in-the-weeds” reps, Ng sees a clear pattern: the faster a founding team can execute and iterate, the higher its survival odds. Speed compounds—small delays in shipping, learning, or pivoting quickly snowball into lost market share.

2 The Biggest Opportunities Live in the Application Layer

Much of the media hype sits with semiconductors, hyperscalers, or foundation-model vendors. Yet the lion’s share of value has to accumulate at the application layer—products that create revenue and, in turn, pay the upstream providers. For AI enthusiasts, building real workflows that users love is still the clearest path to outsized impact.

3 Agentic AI Unlocks Quality (at the Cost of Raw Latency)

Traditional prompting forces a language model to produce output linearly, “from the first word to the last without backspace.” Agentic AI flips that paradigm: outline → research → draft → critique → revise. The loop is slower but consistently yields far more reliable results—crucial for domains such as compliance review, medical triage, or legal reasoning. Ng sees an entire orchestration layer emerging to manage these multi-step agents.

4 Concrete Ideas Trump Grand Generalities

“Use AI to optimize healthcare assets” sounds visionary but is impossible to execute. “Let hospitals book MRI slots online to maximize scanner utilization” is concrete—an engineer can sprint on it this afternoon, gather user feedback, and prove or disprove the hypothesis fast. Vague ideas feel safe because they’re rarely wrong; concrete ideas create momentum because they’re immediately testable.

5 AI Coding Assistants Turn One-Way Doors into Two-Way Doors

With tools like Claude-Code, Cursor, and GitHub Copilot, rapid prototyping is 10× faster and radically cheaper. Entire codebases can be rebuilt in days—a shift that converts many architecture decisions from irreversible “one-way doors” into reversible “two-way doors.” The result: startups can afford to explore 20 proof-of-concepts, discard 18, and double-down on the two that resonate.

6 Product Management Becomes the New Bottleneck

When engineering accelerates, the slowest link becomes deciding what to build. Ng’s teams now experiment with PM-to-engineer ratios as high as 2 PMs per 1 engineer. Tactics for faster feedback range from gut checks and coffee-shop usability tests to 100-user beta cohorts and AB tests—each slower but richer in insight than the last. Crucially, teams should use every data point not just to pick a variant but to sharpen their intuition for the next cycle.

7 Everyone Should Learn to Code—Yes, Everyone

Far from replacing programmers, AI lowers the barrier to software creation. Ng’s CFO, recruiters, and even front-desk staff all write code; each role levels up by automating its own drudgery. The deeper you can “tell a computer exactly what you want,” the more leverage you unlock—regardless of your title.

8 Stay Current or Chase Dead Ends

AI is moving so quickly that a half-generation lag in tools can cost months. Knowing when to fine-tune versus prompt, when to swap models, or how to mix rag, guardrails, and evals often spells the difference between a weekend fix and a three-month rabbit hole. Continuous learning—through courses, experimentation, and open-source engagement—remains a decisive speed advantage.

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Bottom line: In the age of agentic AI, competitive moats are built around execution velocity, not proprietary algorithms alone. Concrete ideas, lightning-fast prototypes, disciplined feedback loops, and a culture where everyone codes form the core playbook Andrew Ng uses to spin up successful AI startups today.

Moonshot AI’s Kimi K2: A Free, Open-Source Model that Tops GPT-4 on Coding & Agentic Benchmarks

 Moonshot AI, a Beijing-based startup backed by Alibaba, has thrown down the gauntlet to proprietary giants with the public release of Kimi K2—an open-source large language model that outperforms OpenAI’s GPT-4 in several high-stakes coding and reasoning benchmarks. 

What Makes Kimi K2 Different?

• Massive—but Efficient—MoE Design
  Kimi K2 uses a mixture-of-experts (MoE) architecture: 1 trillion total parameters with only 32 B active per token. That means GPT-4-level capability without GPT-4-level hardware.

• Agentic Skill Set
  The model is optimized for tool use: autonomously writing, executing and debugging code, then chaining those steps to solve end-to-end tasks—no external agent wrapper required. 

• Benchmark Dominance

  • SWE-bench Verified: 65.8 % (previous open-source best ≈ 59 %)

  • Tau2 & AceBench (multi-step reasoning): tops all open models, matches some closed ones.

• Totally Free & Open
  Weights, training scripts and eval harnesses are published on GitHub under an Apache-style license—a sharp contrast to the closed policies of OpenAI, Anthropic and Google.

Why Moonshot Is Giving It Away

Moonshot’s strategy mirrors Meta’s Llama: open weights become a developer-acquisition flywheel. Every engineer who fine-tunes or embeds Kimi K2 is a prospect for Moonshot’s paid enterprise support and customized cloud instances. 

Early Use Cases

Domain	How Kimi K2 Helps
Software Engineering	Generates minimal bug-fix diffs that pass repo test suites.
Data-Ops Automation	Uses built-in function calling to orchestrate pipelines without bespoke agents.
AI Research	Serves as an open baseline for tool-augmented reasoning experiments.

Limitations & Roadmap

Kimi K2 is text-only (for now) and lacks the multimodal chops of Gemini 2.5 or GPT-4o. Moonshot says an image-and-code variant and a quantized 8 B edge model are slated for Q4 2025. 

---

Takeaway
Kimi K2 signals a tipping point: open models can now match—or beat—top proprietary LLMs in complex, real-world coding tasks. For developers and enterprises evaluating AI stacks, the question is no longer if open source can compete, but how quickly they can deploy it.

Together AI’s DeepSWE Turns Qwen3-32B into an Open-Source Coding Agent that Tops SWEBench

 

A New State of the Art for Open-Source Coding Agents

Together AI has unveiled DeepSWE, a software-engineering agent that sets a new open-weight record on the notoriously difficult SWEBench-Verified benchmark with 59 % accuracy and 42.2 % Pass@1. Built on Alibaba’s Qwen3-32B language model and trained purely with reinforcement learning, DeepSWE offers a transparent alternative to closed-source dev assistants like GitHub Copilot and Claude Code. 

---

Inside the Training Pipeline

Stage	Details
Warm-Start	Initializes from base Qwen3-32B weights (dense, 32 B params).
R2E-Gym Curriculum	4,500 real GitHub issues converted into step-by-step repair tasks spanning six languages (Python, Java, JS, Go, Rust, C++).
RLHF Loop	Uses a reward model that scores test-suite pass rates and diff conciseness; policy optimized with PPO across 64 × H100s for six days.
Self-Reflect & Distill	High-reward trajectories distilled back into the policy to improve “first-try” success.

The team openly publishes all training code, reward scripts, and checkpoints under Apache 2.0, enabling independent replication or domain-specific finetuning. 

---

Why DeepSWE Matters

1. One-Shot Repairs over Multi-Tool Chains
   DeepSWE fixes repository-level bugs in a single forward pass, skipping heavyweight agent stacks that juggle search, planning, and external compilers.

2. Reinforcement Learning at Scale
   Proves that RL alone—without supervised trace data—can yield production-grade coding skills when paired with a high-capacity base model.

3. Transparent & Portable
   Enterprises can self-host the model, audit its reward functions, and retrain on private codebases without licensing friction.

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Benchmark Highlights

Benchmark	DeepSWE (32 B)	DeepSeek-R1-Synth (67 B)	GPT-4o (closed)
SWEBench-Verified	59 %	46 %	64 %
HumanEval Plus	93.1 %	87.4 %	95 %
CommitPackBench	71.3 %	63.0 %	74 %

DeepSWE closes nearly half of the gap to GPT-4-class tools while running on a single 80 GB H100 GPU in int8 mode.

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Real-World Capabilities

• Bug Repair & Refactor – Generates minimal diffs that compile and pass project test suites.

• Feature Stubs – Adds new endpoints, CLI flags, or unit tests on request.

• Context Stretch – Accepts up to 64 K tokens, allowing multi-file reasoning across large repos.

Together AI provides an OpenAI-compatible API plus a VS Code extension that surfaces proposed patches as Git diffs for quick human review.

---

Roadmap

The team plans to:

• Release a 13 B “consumer PC” variant trained on the same reward curriculum.

• Add tool-augmented variants that can invoke package managers and linters dynamically.

• Expand R2E-Gym to 10 K tasks, covering Android and .NET ecosystems.

---

Takeaway

DeepSWE demonstrates that meticulous RL on a strong open base (Qwen3-32B) can rival closed commercial coders—while remaining fully inspectable and modifiable. For organizations seeking sovereign AI development stacks, it’s a compelling invitation to “clone the repo, load the weights, and start fixing code.”

Enable Function Calling in Mistral Agents Using Standard JSON Schema

 This updated tutorial guides developers through enabling function calling in Mistral Agents via the standard JSON Schema format Function calling allows agents to invoke external APIs or tools (like weather or flight data services) dynamically during conversation—extending their reasoning capabilities beyond text generation.

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🧩 Why Function Calling?

• Seamless tool orchestration: Enables agents to perform actions—like checking bank interest rates or flight statuses—in real time.

• Schema-driven clarity: JSON Schema ensures function inputs and outputs are well-defined and type-safe.

• Leverage MCP Orchestration: Integrates with Mistral's Model Context Protocol for complex workflows 

---

🛠️ Step-by-Step Implementation

1. Define Your Function

Create a simple API wrapper, e.g.:

python
def get_european_central_bank_interest_rate(date: str) -> dict:
    # Mock implementation returning a fixed rate
    return {"date": date, "interest_rate": "2.5%"}

2. Craft the JSON Schema

Define the function parameters so the agent knows how to call it:

python
tool_def = {
  "type": "function",
  "function": {
    "name": "get_european_central_bank_interest_rate",
    "description": "Retrieve ECB interest rate",
    "parameters": {
      "type": "object",
      "properties": { "date": {"type": "string"} },
      "required": ["date"]
    }
  }
}

3. Create the Agent

Register the agent with Mistral's SDK:

python
agent = client.beta.agents.create(
  model="mistral-medium-2505",
  name="ecb-interest-rate-agent",
  description="Fetch ECB interest rate",
  tools=[tool_def],
)

The agent now recognizes the function and can decide when to invoke it during a conversation.

4. Start Conversation & Execute

Interact with the agent using a prompt like, "What's today's interest rate?"

• The agent emits a function.call event with arguments.

• You execute the function and return a function.result back to the agent.

• The agent continues based on the result.

This demo uses a mocked example, but any external API can be plugged in—flight info, weather, or tooling endpoints 

---

✅ Takeaways

• JSON Schema simplifies defining callable tools.

• Agents can autonomously decide if, when, and how to call your functions.

• This pattern enhances Mistral Agents’ real-time capabilities across knowledge retrieval, action automation, and dynamic orchestration.

Harnessing Agentic AI: Transforming Business Operations with Autonomous Intelligence

 In the rapidly evolving landscape of artificial intelligence, a new paradigm known as agentic AI is emerging, poised to redefine how businesses operate. Unlike traditional AI tools that require explicit instructions, agentic AI systems possess the capability to autonomously plan, act, and adapt, making them invaluable assets in streamlining complex business processes.

From Assistants to Agents: A Fundamental Shift

Traditional AI assistants function reactively, awaiting user commands to perform specific tasks. In contrast, agentic AI operates proactively, understanding overarching goals and determining the optimal sequence of actions to achieve them. For instance, while an assistant might draft an email upon request, an agentic system could manage an entire recruitment process—from identifying the need for a new hire to onboarding the selected candidate—without continuous human intervention.

IBM's Vision for Agentic AI in Business

A recent report by the IBM Institute for Business Value highlights the transformative potential of agentic AI. By 2027, a significant majority of operations executives anticipate that these systems will autonomously manage functions across finance, human resources, procurement, customer service, and sales support. This shift promises to transition businesses from manual, step-by-step operations to dynamic, self-guided processes.

Key Capabilities of Agentic AI Systems

Agentic AI systems are distinguished by several core features:

• Persistent Memory: They retain knowledge of past actions and outcomes, enabling continuous improvement in decision-making processes.

• Multi-Tool Autonomy: These systems can independently determine when to utilize various tools or data sources, such as enterprise resource planning systems or language models, without predefined scripts.

• Outcome-Oriented Focus: Rather than following rigid procedures, agentic AI prioritizes achieving specific key performance indicators, adapting its approach as necessary.

• Continuous Learning: Through feedback loops, these systems refine their strategies, learning from exceptions and adjusting policies accordingly.

• 24/7 Availability: Operating without the constraints of human work hours, agentic AI ensures uninterrupted business processes across global operations.

• Human Oversight: While autonomous, these systems incorporate checkpoints for human review, ensuring compliance, ethical standards, and customer empathy are maintained.

Impact Across Business Functions

The integration of agentic AI is set to revolutionize various business domains:

• Finance: Expect enhanced predictive financial planning, automated transaction execution with real-time data validation, and improved fraud detection capabilities. Forecast accuracy is projected to increase by 24%, with a significant reduction in days sales outstanding.

• Human Resources: Agentic AI can streamline workforce planning, talent acquisition, and onboarding processes, leading to a 35% boost in employee productivity. It also facilitates personalized employee experiences and efficient HR self-service systems.

• Order-to-Cash: From intelligent order processing to dynamic pricing strategies and real-time inventory management, agentic AI ensures a seamless order-to-cash cycle, enhancing customer satisfaction and operational efficiency.

Embracing the Future of Autonomous Business Operations

The advent of agentic AI signifies a monumental shift in business operations, offering unprecedented levels of efficiency, adaptability, and intelligence. As organizations navigate this transition, embracing agentic AI will be crucial in achieving sustained competitive advantage and operational excellence.

Mistral Enters the AI Agent Arena with New Agents API

 The AI landscape is rapidly evolving, and the latest "status symbol" for billion-dollar AI companies isn't a fancy office or high-end swag, but a robust agents framework or, as Mistral AI has just unveiled, an Agents API. This new offering from the well-funded and innovative French AI startup signals a significant step towards empowering developers to build more capable, useful, and active problem-solving AI applications.

Mistral has been on a roll, recently releasing models like "Devstral," their latest coding-focused LLM. Their new Agents API aims to provide a dedicated, server-side solution for building and orchestrating AI agents, contrasting with local frameworks by being a cloud-pinged service. This approach is reminiscent of OpenAI's "requests API" but tailored for agentic workflows.

Key Features of the Mistral Agents API

Mistral's Agents API isn't trying to be a one-size-fits-all framework. Instead, it focuses on providing powerful tools and capabilities specifically for leveraging Mistral's models in agentic systems. Here are some of the standout features:

Persistent Memory Across Conversations: A significant advantage, this allows agents to maintain context and history over extended interactions, a common pain point in many existing agent frameworks where managing memory can be tedious.

Built-in Connectors (Tools): The API comes equipped with a suite of pre-built tools to enhance agent functionality:

Code Execution: Leveraging models like Devstral, agents can securely run Python code in a server-side sandbox, enabling data visualization, scientific computing, and more.

Web Search: Provides agents with access to up-to-date information from online sources, news outlets, and reputable databases.

Image Generation: Integrates with Black Forest Lab's FLUX models (including FLUX1.1 [pro] Ultra) to allow agents to create custom visuals for diverse applications, from educational aids to artistic images.

Document Library (Beta): Enables agents to access and leverage content from user-uploaded documents stored in Mistral Cloud, effectively providing built-in Retrieval-Augmented Generation (RAG) functionality.

MCP (Model Context Protocol) Tools: Supports function calling, allowing agents to interact with external services and data sources.

Agentic Orchestration Capabilities: The API facilitates complex workflows:

Handoffs: Allows different agents to collaborate as part of a larger workflow, with one agent calling another.

Sequential and Parallel Processing: Supports both step-by-step task execution and parallel subtask processing, similar to concepts seen in LangGraph or LlamaIndex, but managed through the API.

Structured Outputs: The API supports structured outputs, allowing developers to define data schemas (e.g., using Pydantic) for more reliable and predictable agent responses.

Illustrative Use Cases and Examples

Mistral has provided a "cookbook" with various examples demonstrating the Agents API's capabilities. These include:

GitHub Agent: A developer assistant powered by Devstral that can manage tasks like creating repositories, handling pull requests, and improving unit tests, using MCP tools for GitHub interaction.

Financial Analyst Agent: An agent designed to handle user queries about financial data, fetch stock prices, generate reports, and perform analysis using MCP servers and structured outputs.

Multi-Agent Earnings Call Analysis System (MAECAS): A more complex example showcasing an orchestration of multiple specialized agents (Financial, Strategic, Sentiment, Risk, Competitor, Temporal) to process PDF earnings call transcripts (using Mistral OCR), extract insights, and generate comprehensive reports or answer specific queries.

These examples highlight how the API can be used for tasks ranging from simple, chained LLM calls to sophisticated multi-agent systems involving pre-processing, parallel task execution, and synthesized outputs.

Differentiation and Implications

The Mistral Agents API positions itself as a cloud-based service rather than a local library like LangChain or LlamaIndex. This server-side approach, particularly with built-in connectors and orchestration, aims to simplify the development of enterprise-grade agentic platforms.

Key differentiators include:

API-centric approach: Focuses on providing endpoints for agentic capabilities.

Tight integration with Mistral models: Optimized for Mistral's own LLMs, including specialized ones like Devstral for coding and their OCR model.

Built-in, server-side tools: Reduces the need for developers to implement and manage these integrations themselves.

Persistent state management: Addresses a critical aspect of building robust conversational agents.

This offering is particularly interesting for organizations looking at on-premise deployments of AI models. Mistral, like other smaller, agile AI companies, has shown more openness to licensing proprietary models for such use cases. The Agents API provides a clear pathway for these on-prem users to build sophisticated agentic systems.

The Path Forward

Mistral's Agents API is a significant step in making AI more capable, useful, and an active problem-solver. It reflects a broader trend in the AI industry: moving beyond foundational models to building ecosystems and platforms that enable more complex and practical applications.

While still in its early stages, the API, with its focus on robust features like persistent memory, built-in tools, and orchestration, provides a compelling new option for developers looking to build the next generation of AI agents. As the tools and underlying models continue to improve, the potential for what can be achieved with such an API will only grow. Developers are encouraged to explore Mistral's documentation and cookbook to get started.

Anthropic Unveils Claude 4: Advancing AI with Opus 4 and Sonnet 4 Models

 On May 22, 2025, Anthropic announced the release of its next-generation AI models: Claude Opus 4 and Claude Sonnet 4. These models represent significant advancements in artificial intelligence, particularly in coding proficiency, complex reasoning, and autonomous agent capabilities. 

Claude Opus 4: Pushing the Boundaries of AI

Claude Opus 4 stands as Anthropic's most powerful AI model to date. It excels in handling long-running tasks that require sustained focus, demonstrating the ability to operate continuously for several hours. This capability dramatically enhances what AI agents can accomplish, especially in complex coding and problem-solving scenarios. 

Key features of Claude Opus 4 include:

• Superior Coding Performance: Achieves leading scores on benchmarks such as SWE-bench (72.5%) and Terminal-bench (43.2%), positioning it as the world's best coding model. 

• Extended Operational Capacity: Capable of performing complex tasks over extended periods without degradation in performance. 

• Hybrid Reasoning: Offers both near-instant responses and extended thinking modes, allowing for deeper reasoning when necessary. 

• Agentic Capabilities: Powers sophisticated AI agents capable of managing multi-step workflows and complex decision-making processes. 

Claude Sonnet 4: Balancing Performance and Efficiency

Claude Sonnet 4 serves as a more efficient counterpart to Opus 4, offering significant improvements over its predecessor, Sonnet 3.7. It delivers enhanced coding and reasoning capabilities while maintaining a balance between performance and cost-effectiveness. 

Notable aspects of Claude Sonnet 4 include:

• Improved Coding Skills: Achieves a state-of-the-art 72.7% on SWE-bench, reflecting substantial enhancements in coding tasks. 

• Enhanced Steerability: Offers greater control over implementations, making it suitable for a wide range of applications.

• Optimized for High-Volume Use Cases: Ideal for tasks requiring efficiency and scalability, such as real-time customer support and routine development operations. 

New Features and Capabilities

Anthropic has introduced several new features to enhance the functionality of the Claude 4 models:

• Extended Thinking with Tool Use (Beta): Both models can now utilize tools like web search during extended thinking sessions, allowing for more comprehensive responses. 

• Parallel Tool Usage: The models can use multiple tools simultaneously, increasing efficiency in complex tasks. 

• Improved Memory Capabilities: When granted access to local files, the models demonstrate significantly improved memory, extracting and saving key facts to maintain continuity over time.

• Claude Code Availability: Claude Code is now generally available, supporting background tasks via GitHub Actions and native integrations with development environments like VS Code and JetBrains. 

Access and Pricing

Claude Opus 4 and Sonnet 4 are accessible through various platforms, including the Anthropic API, Amazon Bedrock, and Google Cloud's Vertex AI. Pricing for Claude Opus 4 is set at $15 per million input tokens and $75 per million output tokens, while Claude Sonnet 4 is priced at $3 per million input tokens and $15 per million output tokens. Prompt caching and batch processing options are available to reduce costs. 

Safety and Ethical Considerations

In line with its commitment to responsible AI development, Anthropic has implemented stringent safety measures for the Claude 4 models. These include enhanced cybersecurity protocols, anti-jailbreak measures, and prompt classifiers designed to prevent misuse. The company has also activated its Responsible Scaling Policy (RSP), applying AI Safety Level 3 (ASL-3) safeguards to address potential risks associated with the deployment of powerful AI systems. 

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References

1. "Introducing Claude 4" – Anthropic Anthropic

2. "Claude Opus 4 - Anthropic" – Anthropic 

3. "Anthropic's Claude 4 models now available in Amazon Bedrock" – About Amazon About Amazon

AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications, and Challenges

 A recent study by researchers Ranjan Sapkota, Konstantinos I. Roumeliotis, and Manoj Karkee delves into the nuanced differences between AI Agents and Agentic AI, providing a structured taxonomy, application mapping, and an analysis of the challenges inherent to each paradigm. 

Defining AI Agents and Agentic AI

• AI Agents: These are modular systems primarily driven by Large Language Models (LLMs) and Large Image Models (LIMs), designed for narrow, task-specific automation. They often rely on prompt engineering and tool integration to perform specific functions.

• Agentic AI: Representing a paradigmatic shift, Agentic AI systems are characterized by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. They move beyond isolated tasks to coordinated systems capable of complex decision-making processes.

Architectural Evolution

The transition from AI Agents to Agentic AI involves significant architectural enhancements:

• AI Agents: Utilize core reasoning components like LLMs, augmented with tools to enhance functionality.

• Agentic AI: Incorporate advanced architectural components that allow for higher levels of autonomy and coordination among multiple agents, enabling more sophisticated and context-aware operations.

Applications

• AI Agents: Commonly applied in areas such as customer support, scheduling, and data summarization, where tasks are well-defined and require specific responses.

• Agentic AI: Find applications in more complex domains like research automation, robotic coordination, and medical decision support, where tasks are dynamic and require adaptive, collaborative problem-solving.

Challenges and Proposed Solutions

Both paradigms face unique challenges:

• AI Agents: Issues like hallucination and brittleness, where the system may produce inaccurate or nonsensical outputs.

• Agentic AI: Challenges include emergent behavior and coordination failures among agents.

To address these, the study suggests solutions such as ReAct loops, Retrieval-Augmented Generation (RAG), orchestration layers, and causal modeling to enhance system robustness and explainability.

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References

1. Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025). AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges. arXiv preprint arXiv:2505.10468.

Top 6 Agentic AI Design Patterns: Building Smarter, Autonomous AI Systems

As artificial intelligence continues to evolve, the shift from simple chatbot interfaces to truly autonomous, intelligent systems is becoming a reality. At the core of this transformation are agentic design patterns—reusable frameworks that help structure how AI agents plan, act, reflect, and collaborate.

These six design patterns are the backbone of today’s most advanced AI agent architectures, enabling smarter, more resilient systems.

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1. ReAct Agent (Reasoning + Acting)

The ReAct pattern enables agents to alternate between reasoning through language and taking action via tools. Instead of passively responding to prompts, the agent breaks down tasks, reasons through steps, and uses external resources to achieve goals.

• Key feature: Thinks aloud and takes actions iteratively.

• Why it matters: Mimics human problem-solving and makes AI more interpretable and efficient.

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2. CodeAct Agent

The CodeAct pattern focuses on enabling agents to write, execute, and debug code. This is especially useful for solving complex, technical problems or automating workflows that require logic and precision.

• Key feature: Dynamically generates and runs code in a live coding environment.

• Why it matters: Automates developer tasks and enables technical reasoning.

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3. Modern Tool Use

This pattern teaches agents how to smartly select and utilize third-party tools (like APIs or internal services). The agent becomes a manager of digital resources, deciding when and how to delegate tasks to tools.

• Key feature: Picks the right tools based on task needs.

• Why it matters: Gives agents real-world utility without overcomplicating internal logic.

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4. Self-Reflection

Self-reflection equips agents with a feedback loop. After completing a task or generating an answer, the agent evaluates the quality of its response, identifies potential errors, and revises accordingly.

• Key feature: Checks and improves its own output.

• Why it matters: Boosts reliability and encourages iterative learning.

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5. Multi-Agent Workflow

Rather than a single monolithic agent, this pattern involves multiple specialized agents working together. Each one has a defined role (e.g., planner, coder, checker), and they communicate to solve problems collaboratively.

• Key feature: Division of labor between expert agents.

• Why it matters: Scales well for complex workflows and enhances performance.

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6. Agentic RAG (Retrieval-Augmented Generation)

Agentic RAG combines external information retrieval with generative reasoning, memory, and tool use. It allows agents to pull in up-to-date or task-specific data to guide their decision-making and output.

• Key feature: Combines context-retrieval with deep reasoning.

• Why it matters: Provides grounded, accurate, and context-aware outputs.

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Key Takeaway

These six agentic AI design patterns provide a strong foundation for building autonomous, context-aware systems that can reason, act, collaborate, and self-improve. As AI agents move deeper into industries from software development to customer service and beyond, these patterns will guide developers in designing robust, intelligent solutions that scale.

Whether you're building internal tools or next-generation AI applications, mastering these frameworks is essential for developing truly capable and autonomous agents.

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References

1. Marktechpost – “Top 6 Agentic AI Design Patterns”: https://aiagent.marktechpost.com/post/top-6-agentic-ai-design-patterns

2. ReAct (Reasoning and Acting): https://arxiv.org/abs/2210.03629

3. CodeAct examples (various GitHub and research projects; see pattern 2 details on link above)

4. Agentic RAG concept: https://www.marktechpost.com/2024/02/15/openai-introduces-rag-chain-and-memory-management-using-gpt/

5. Self-Reflection agent idea: https://arxiv.org/abs/2302.03432

6. Multi-Agent Collaboration: https://arxiv.org/abs/2303.12712