At face value, the role of a software engineer is to write code that creates and delivers features to solve customer problems. While coding is a fundamental skill, engineering is a far more involved discipline.
Engineering teams face a core conflict between two competing priorities:
- Rapid feature delivery: The pressure to quickly ship new, high quality functionality.
- Operational stability: The ongoing responsibility for maintaining existing systems, fixing production issues, and supporting customers.
This tension, often constrained by limited resources, makes it difficult to dedicate time to the essential technical best practices required for long term system health. This balancing act is the art of making informed trade offs to deliver value without compromising long term stability.
This complexity doesn't just exist at the individual level; it needs to be managed and scaled across entire teams and departments. Each engineer brings their own principles, disciplines, and goals to the table, which must be aligned with departmental and business goals. The effectiveness with which an organization manages these variables is a measure of its engineering maturity.
This isn't a tangible metric you can find on a dashboard, but rather an assessment of how well the organization balances priorities against the company's overarching strategy and the processes and structures in place to maintain best practices. A mature engineering organization moves in sync, making deliberate, collective decisions that propel the business forward. This creates a difficult question: How can a team manage the immense operational burden while also adopting the technical best practices that are crucial for long term health but exist outside the immediate cycle of feature delivery?
Accelerating engineering maturity with AI
This is where the strategic implementation of AI, specifically through AI agents, can be a game changer. By leveraging agents, teams can rapidly automate many of the operational best practices that are characteristic of mature, well resourced engineering organizations.
AI however is not a silver bullet for poor cultural practices or a substitute for thoughtful change management. It does provide a powerful mechanism to quickly test and pilot different operational approaches that would otherwise be too resource intensive or disruptive to implement manually. By tasking AI agents with specific aspects of the engineering process, teams can experiment with and validate best practices, seeing how they function within their unique workflows without committing to a massive, top down overhaul.
Automating best practices with AI agents
Many essential engineering practices are difficult to implement consistently because they are time consuming and require significant manual effort. Here’s how AI agents can automate and enhance some of the most challenging ones.
1. The operational readiness steward agent
- The challenge: A weekly operational review meeting, where the team reviews system health, is critical. However, these meetings are often inefficient. The first half is spent just loading dashboards, adjusting time windows, and manually hunting for anomalies, leaving little time for strategic discussion.
- The AI agent solution: Instead of trying to replace this vital human-centric meeting, this agent acts as a preparatory analyst. It runs a few hours before the scheduled meeting and connects to all our monitoring and logging platforms (like Datadog, Prometheus, and Splunk).
- Data detection: It automatically detects and flags significant anomalies in key metrics (e.g., a spike in p99 latency, a dip in transaction success rate).
- Data correlation: It attempts to correlate events, noting, for example, that a memory usage increase coincided with a specific feature deployment.
List actionable insights: It then generates a concise "Operational Digest" and posts it to the team's Slack channel. The team walks into the meeting with a pre-built agenda of the most important talking points, enabling them to spend the entire time on problem-solving, not data hunting.
2. The ticket triage agent
- The challenge: When a new bug report or feature request is created in a project management tool like Jira, it's often missing key information. It might be a duplicate of an existing ticket, lack clear steps to reproduce, or be assigned to the wrong team, requiring a project manager to manually clean it up.
- The AI agent solution: This agent listens for when a ticket is created from your project management tool. This Agent would:
- Duplicate check: When a new ticket is created, the agent takes the title and description and Searches for semantically similar existing tickets. If a likely duplicate is found, it posts an automatic comment: "This might be a duplicate of TICKET-123. Can you confirm?"
- Information validation: The agent scans the description for key phrases like "steps to reproduce," "expected behavior," and "actual behavior." If they're missing, it can add a "needs-info" label and leave a comment: "To help us resolve this faster, could you please add clear steps to reproduce the issue?"
- Suggest ownership: Based on the ticket's content (e.g., mentions of "payment," "checkout," "invoice"), it can analyze the text and suggest the right team to assign it to, posting a comment like "Based on the content, this seems related to the @payments-team."
3. The documentation draft agent
- The challenge: Developers often forget or don't have the time to update documentation after merging a significant code change. This leads to documentation becoming stale and untrustworthy, while the manual process of finding the right page and writing the update creates friction.
- The AI agent solution: This agent acts as an assistant to ensure documentation keeps pace with code changes, without the risk of making incorrect autonomous edits.
- PR code change analysis: When a PR is merged, the agent analyzes the code changes and the PR description to understand the update.
- Document finder: It uses a search function to identify the most relevant page in your internal documentation (e.g., Confluence or a wiki).
- Draft generator: The agent then generates a concise, human readable summary of the change.
- Editor: Instead of editing the document directly, it posts a comment on the relevant page, tagging the PR author with the suggested text. This allows the developer to quickly review the suggestion for accuracy and copy it into the document, turning a multi-step process into a simple verification task.
- Can also be used in conjunction with the PR Guardian Agent.
Augmentation, not replacement
It's crucial to understand that these AI agents are not replacements for people, nor do they eliminate the need for sound processes and a healthy engineering culture. An AI agent cannot have a creative architectural debate or mentor a junior developer. Instead, these tools augment and empower the team. They handle the repetitive, detail oriented, and time consuming tasks that humans often find tedious and are prone to getting wrong.
By offloading this cognitive burden, engineers can focus on what they do best: solving complex problems, designing elegant systems, and creating innovative products. This framework allows an organization to test and fail quickly with operational strategies. Instead of spending six months and countless meetings to roll out a new manual review process, a team can deploy an AI agent in a "shadow mode" for a few weeks, gather data on its effectiveness, and then decide whether to integrate it fully. This agility enables teams to evolve their maturity rapidly, refining their processes and improving their quality of life, one automated agent at a time.
Want to learn more about Glean and how some of the best engineering teams in the field are leveraging AI? Check out the Gleaniverse or get a free demo of Glean today!
