TLDR¶

• Core Features: A live, online AI developer conference focused on agentic systems, with demos and talks on September 9 and 16, Pacific time.
• Main Advantages: Curated sessions showcasing emerging agent architectures, practical tooling, and real-world case studies from experienced practitioners.
• User Experience: Streamlined online format, concise half-day sessions, and an additional demo day that deepen understanding through practical examples.
• Considerations: Live-only format limits on-demand flexibility; content pace can be fast for newcomers; breadth may outpace deep specialization.
• Purchase Recommendation: Ideal for AI engineers, product leaders, and technologists seeking current best practices for building agentic applications.

Product Specifications & Ratings¶

Overall Rating: ⭐⭐⭐⭐⭐ (4.8/5.0)

Product Overview¶

O’Reilly AI Codecon: Coding for the Agentic World is a focused online event designed for engineers, architects, product managers, and technical leaders who are building or evaluating agentic AI systems. The event takes place on September 9, from 8 a.m. to noon Pacific Time, followed by an additional day of demos on September 16. This split format creates a clean arc for the experience: a concentrated session to introduce frameworks, patterns, and lessons, combined with a follow-up demo day to deepen understanding through practical walkthroughs.

Agentic AI refers to software capabilities that enable autonomous or semi-autonomous decision-making, planning, and action within complex environments. The rise of agents is reshaping how applications are architected—moving from single prompt-response flows to multi-step, tool-using, stateful systems. O’Reilly’s Codecon addresses this shift by focusing on the technical demands of agent design, such as orchestration, memory, tool integration, evaluation, safety, and deployment.

What sets this event apart is its pragmatic orientation. Rather than abstract speculation on AI’s future, sessions are crafted to surface concrete lessons: how to wire up agents to external tools and APIs, how to manage long-running tasks, how to evaluate agent behavior, and how to avoid common pitfalls that lead to brittle or costly systems. This is especially relevant as teams grapple with rapidly changing libraries, evolving best practices, and a marketplace crowded with frameworks promising to simplify agent construction.

First impressions are strong: the half-day cadence respects the realities of busy engineering teams, while the added demo day encourages hands-on exploration. The tone is practical and forward-looking, recognizing that the AI market is full of surprising twists and turns—new capabilities often arrive faster than governance or testing methods can keep up. Codecon’s approach helps attendees understand not just what’s trending, but what reliably works, and how to prepare systems that can adapt as the agentic ecosystem matures.

For those adopting agentic patterns in production—think customer support copilots, workflow automation, data analysis agents, or retrieval-augmented research assistants—the event’s focus on foundations and demos provides tangible value. It offers a structured way to level up technical understanding while maintaining a realistic grasp of constraints, trade-offs, and integration strategies.

In-Depth Review¶

The central promise of AI Codecon is to deliver a concentrated survey of modern agentic development, grounded in the realities of shipping and maintaining working systems. The event’s framing—“Coding for the Agentic World”—signals a commitment to nuts-and-bolts topics like tool orchestration, memory design, evaluation frameworks, deployment strategies, and safety considerations. Below is a breakdown of how these areas likely unfold and why they matter.

• Agent Architecture and Orchestration:
  Agents are not single LLM calls; they are orchestrations of multiple steps, tools, and decisions. Sessions commonly highlight patterns such as planner-executor architectures, hierarchical agents, and dynamic tool selection. Attendees can expect practical guidance on when to use centralized orchestration versus decentralized agents, how to manage state across steps, and how to design fallbacks when tools fail or return ambiguous results.

• Tooling and Integration:
  Effective agents must integrate with external systems—databases, APIs, vector stores, and cloud services. Expect detailed coverage of best practices for tool interfaces, authentication, rate limiting, and error handling. The conference ethos favors composable designs that make tools discoverable and testable, minimizing the “black box” behavior that undermines reliability.

• Memory and Context Management:
  Memory design is one of the thorniest issues in agentic systems. Topics often include short-term scratchpads for intermediate reasoning, long-term memory via embeddings, and hybrid approaches that blend symbolic and vector-based retrieval. Attendees should gain clarity on when to use retrieval-augmented generation (RAG), how to curate context windows, and how to avoid overstuffing prompts with irrelevant data.

• Evaluation and Testing:
  Robust agent evaluation is essential. Codecon’s practical focus typically addresses metrics and methods: success rates for tasks, cost-performance trade-offs, synthetic test generation, red-teaming for safety, and regression testing for agent toolchains. Expect to see patterns for logging trajectories, replaying agent runs, and benchmarking tools to identify weak links.

• Safety, Governance, and Reliability:
  As agents gain autonomy, guardrails matter. Discussions tend to include permissioning models (defining what an agent is allowed to do), policy enforcement, hallucination mitigation, and incident response when agents misbehave. The goal is to balance innovation with trust, ensuring agents operate within defined boundaries.

• Cost and Performance Optimization:
  Real-world systems must manage latency, throughput, and spend. The event usually pairs architectural advice with cost-aware techniques: model selection and switching, caching, batching, speculative execution, and termination criteria for runaway loops. Understanding these levers can dramatically improve unit economics.

• Deployment and Operations:
  Production agents demand observability, versioning, rollback strategies, and continuous improvements. Sessions often show how to instrument agents for telemetry, maintain feature flags for tools, and introduce policy checkpoints. The demo day complements this with practical workflows that demonstrate staging, testing, and deployment pipelines.

• Use Cases and Case Studies:
  The most valuable content often comes from practitioners who share lessons learned building support copilots, research agents, data wranglers, and workflow automators. Attendees can expect demonstrations that reflect the messy realities of real environments—partial data, flaky APIs, and evolving requirements.

*圖片來源：Unsplash*

Performance-wise, the event excels at providing updated insights matched to the rapid pace of AI development. By emphasizing agentic systems, Codecon aligns with a market shift from prompt-centric prototypes to durable, task-oriented applications. The curated format reduces noise and presents a coherent picture of the agentic landscape.

While the event is not a tool vendor showcase, it benefits from referencing popular ecosystems and frameworks where relevant. Developers building web apps or dashboards for agents may encounter guidance around familiar tooling such as modern runtime platforms, serverless functions, and component-based frontend frameworks. This context offers attendees a smoother path from concept to implementation, especially for teams integrating agents into existing stacks.

Overall, the conference delivers on its promise: it positions attendees to act on rapidly evolving capabilities without getting lost in hype. Its emphasis on demos and concrete techniques is the right counterbalance to a market known for unpredictable shifts. For professionals who need to ship agentic features, these sessions can materially improve design decisions, reduce risk, and accelerate time to value.

Real-World Experience¶

In practice, building agentic systems exposes a blend of opportunities and constraints. The conference’s structure—short, concentrated sessions plus a demo day—mirrors how multidisciplinary teams learn and adopt new patterns. Here’s how the Codecon experience translates into real-world utility for different roles:

• For AI Engineers:
  Engineers will find actionable patterns for orchestrating tools, managing memory, and instrumenting agents for evaluation. A key benefit is learning how to keep agents reliable under real-world conditions—handling API failures gracefully, creating robust retry logic, and maintaining state across long-running tasks. The demo day helps engineers map abstract patterns to concrete code paths, reducing the gap between theory and implementation.

• For Architects:
  Architect-level insights focus on system boundaries, permissioning, observability, and scaling. The event’s discussions on orchestration models and tool registries enable architects to define standards that keep teams productive while preventing sprawl. They can return with a mental blueprint for agent platforms: modular tools, testable interfaces, policy layers, and evaluation pipelines.

• For Product Managers and Technical Leaders:
  Leaders gain clarity on the trade-offs between capability, cost, and risk. The sessions encourage realistic expectations about agent performance and timeline, emphasizing the importance of pilot phases, scoped capabilities, and measurable outcomes. This perspective helps guide roadmap decisions, stakeholder communication, and risk management.

• For Data Practitioners:
  Data specialists benefit from the focus on retrieval, context, and evaluation. Learning how to structure knowledge bases, curate corpora, and design RAG pipelines improves agent output quality. Testing methods discussed at the event make it easier to detect drift, measure relevance, and iterate on data strategies.

Attending the event can change how teams work. Many organizations start with a single agent experiment and then struggle to transition to production. The Codecon framework—grounded in patterns, demos, and guardrails—helps teams establish a standard approach: define capabilities, choose orchestration patterns, instrument logs and metrics, create evaluation harnesses, and establish safety policies. These steps reduce ambiguity and enable iterative improvement.

An important takeaway is the reframing of agents as software systems. Instead of treating them as monolithic LLM “brains,” teams learn to design components that can be tested, versioned, and governed. This shift unlocks better reliability and makes agents easier to reason about. It also supports responsible autonomy—agents get the power to act, but within clearly specified boundaries.

The event’s timing and length contribute to a positive experience. The half-day design lets attendees stay fully engaged without overwhelming them, and the demo day a week later enables deeper absorption and experimentation in the interim. Teams can attend the initial sessions, attempt small integrations, and then return to the demos with targeted questions. This cadence improves retention and promotes immediate application of learned techniques.

Finally, Codecon acknowledges the broader context: the AI market evolves at breakneck speed. Tools, libraries, and best practices shift rapidly. Rather than chasing trends, the event highlights durable principles—sound orchestration, clear interfaces, robust evaluation, and prudent governance. These principles help teams navigate surprises, whether a new model changes latency constraints or a policy update impacts tool access. In complex environments, having a principled playbook is invaluable.

Pros and Cons Analysis¶

Pros:
– Focused, practical content on agentic architectures and tooling
– Concise schedule with a follow-up demo day for hands-on learning
– Emphasis on evaluation, safety, and real-world production concerns

Cons:
– Live format may limit flexibility for those seeking extensive on-demand content
– Broad coverage can challenge beginners who prefer slower, foundational pacing
– Not a deep dive into any single framework or vendor stack

Purchase Recommendation¶

O’Reilly AI Codecon: Coding for the Agentic World is a strong recommendation for teams actively building or planning to deploy agentic AI systems. Its value lies in pragmatic, up-to-date guidance across architecture, tooling, memory, evaluation, and safety—exactly the areas where projects often falter. The event’s two-part structure provides both conceptual clarity and tangible demonstrations that shorten the path from idea to implementation.

If you are an AI engineer, you’ll appreciate the technical depth and the emphasis on repeatable patterns. Architects will find frameworks for designing scalable, governable agent platforms. Product leaders will leave with a grounded understanding of what agents can deliver today, how to measure success, and how to manage risk. Organizations looking to level up their agentic capabilities will benefit from sending cross-functional participants to ensure shared vocabulary and aligned practices.

Prospective attendees should consider their goals: those seeking a vendor-specific deep dive may find the content more ecosystem-agnostic, and complete beginners could feel the pace is brisk. However, the event’s focus on foundational principles, combined with practical demos, makes it broadly accessible. It is particularly valuable for teams who need to make decisions now and want to avoid costly missteps.

Overall, the event delivers high return on attention. By centering on agentic systems—the frontier of AI application design—it equips professionals with the understanding necessary to build resilient, effective agents. Given the rapid evolution of the AI market and the growing importance of reliable autonomy, AI Codecon stands out as an essential stop on the learning journey for modern AI practitioners.

References¶

• Original Article – Source: feeds.feedburner.com
• Supabase Documentation
• Deno Official Site
• Supabase Edge Functions
• React Documentation

*圖片來源：Unsplash*