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#aiagents

6 posts4 participants0 posts today
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Miguel Afonso Caetano

"My current conclusion, though preliminary in this rapidly evolving field, is that not only can seasoned developers benefit from this technology — they are actually in the optimal position to harness its power.

Here’s the fascinating part: The very experience and accumulated know-how in software engineering and project management — which might seem obsolete in the age of AI — are precisely what enable the most effective use of these tools.

While I haven’t found the perfect metaphor for these LLM-based programming agents in an AI-assisted coding setup, I currently think of them as “an absolute senior when it comes to programming knowledge, but an absolute junior when it comes to architectural oversight in your specific context.”

This means that it takes some strategic effort to make them save you a tremendous amount of work.

And who better to invest that effort in the right way than a senior software engineer?

As we’ll see, while we’re dealing with cutting-edge technology, it’s the time-tested, traditional practices and tools that enable us to wield this new capability most effectively."

manuel.kiessling.net/2025/03/3

The Log Book of Manuel Kießling · Senior Developer Skills in the AI Age: Leveraging Experience for Better Results • Manuel KießlingHow time-tested software engineering practices amplify the effectiveness of AI coding assistants.
#AI#GenerativeAI#Programming
Public
Beth Pariseau

Excited to kick off Season 3 of my #ITOps Query #podcast with guest @mipsytipsy. Topics included #AIobservability, #observability -driven development, "Observability 2.0," and Majors' warning / call to action for SREs about the coming wave of #AIagents: "I don't want to see happen to #SRE what happened to QA 15 years ago." #o11y #agenticAI #softwaredevelopment

podbean.com/ew/pb-m2rkt-1854d3

www.podbean.comCharity Majors on AI observability and a call to action for SREsCharity Majors pioneered the term 'observability' as co-founder and CTO of Honeycomb.io, based on her experience building and managing distributed systems at Parse, Facebook, and Linden Lab building Second Life. She is the co-author of the O'Reilly books Observability Engineering and Database Reliability Engineering. Her Honeycomb bio adds that she "loves free speech, free software and single malt scotch." In this interview with Informa TechTarget senior news writer Beth Pariseau, Majors covers the interplay between AI and observability, "Observability 2.0," and urges site reliability engineers to lean into AI agents, even if they seem to be replacements.
Public
Miguel Afonso Caetano

One thing that is strangely missing from this blogpost is the comparative resource efficiency of each workflow, based on a standard fixed metric. But I imagine that for Anthropic that's not a problem ;-)

"Success in the LLM space isn't about building the most sophisticated system. It's about building the right system for your needs. Start with simple prompts, optimize them with comprehensive evaluation, and add multi-step agentic systems only when simpler solutions fall short.

When implementing agents, we try to follow three core principles:

- Maintain simplicity in your agent's design.

- Prioritize transparency by explicitly showing the agent’s planning steps.

- Carefully craft your agent-computer interface (ACI) through thorough tool documentation and testing.

- Frameworks can help you get started quickly, but don't hesitate to reduce abstraction layers and build with basic components as you move to production. By following these principles, you can create agents that are not only powerful but also reliable, maintainable, and trusted by their users."

anthropic.com/engineering/buil

www.anthropic.comBuilding Effective AI Agents | AnthropicDiscover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI.
#AI#AIAgents#AgentWorkflows
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