Best AI tools for software engineers: how to build the right AI stack

The AI coding tools worth adopting don’t just write code fast — they combine code generation, organizational context, incident response, and governance so engineering teams can ship faster, without trading speed for rework, trust problems, or security risk.

GitHub Copilot now writes nearly half of the average developer’s code, and AI-powered coding assistants like Cursor, Claude Code, and Windsurf are becoming default starting points. But coding speed was never the real bottleneck. The hard part — assembling the right context from tickets, design docs, logs, incidents, ownership records, and Slack threads — still falls on the engineer. When AI tools lack that context, saved typing time comes back as downstream fixes and integration debt.

Engineering leaders describe this problem in three ways:

• An outcome gap. In a METR randomized controlled trial of experienced open-source developers, tasks where participants used AI coding tools took 19% longer to complete — even though the developers expected to be 24% faster. A follow-up study with a larger cohort, begun in August 2025, showed mixed results complicated by selection bias, but the perception gap persisted across both studies: developers consistently overestimate AI's impact on their speed.
• A trust gap. More developers actively distrust AI accuracy than trust it (46% vs. 33%, per the 2025 Stack Overflow Developer Survey), and 66% say their biggest frustration is AI-generated code that's "almost right, but not quite."
• A safety gap. Close to 45% of AI-generated code introduces at least one OWASP Top 10 security vulnerability, according to the 2025 Veracode GenAI Code Security Report.

Closing these gaps takes more than a better model in your integrated development environment (IDE). It takes an AI coding stack — and knowing which tools belong in it. This post walks through what to evaluate, the strongest AI developer tools by category, and how to build an AI stack that fits your team's development process.

What to look for in an AI coding tool for software engineers

Before diving into specific tools, it helps to set evaluation criteria for AI assistance that go beyond feature lists. These questions surface how well a tool fits enterprise engineering work — and they apply equally to AI coding assistants, context platforms, observability AI, and agent frameworks.

What context does an AI tool actually have?

Most tools claim to be “context-aware,” but the real question is: context over what? A tool that only sees the current repo or buffer will miss project specific rules and constraints from tickets, designs, and other services. Look for tools that can surface context across Slack, incident tickets, code changes, ownership records, and past incidents — without someone linking that together by hand. For reference, 41% of developers say their biggest productivity drain is outdated, inconsistent, or siloed information. Any tool operating in isolation adds to that problem.

How is trust earned, not assumed?

Engineers won't rely on a system that behaves like a black box. If answers aren't grounded in your own artifacts — code, tickets, logs — with links back to sources, people end up redoing the work just to check it. Adoption stalls.

Where does your data go, and who can act on it?

Once AI can read source code files, logs, and tickets — and especially once it can take actions — you’re making a code security and governance decision, not just a tooling decision. Look at where indexes are hosted (your tenant or multi-tenant); whether the tool supports local models or requires cloud inference; whether code and logs stay inside your VPC with tight egress controls; whether the vendor offers zero data retention policies; and whether all queries and actions are logged for audit. If you can't answer these cleanly, your security team will likely block rollout.

Does it fit the way your teams already work?

The best tools fold into existing development workflows. The worst ask engineers to live in a new tab. Does your tool integrate cleanly with GitHub or GitLab, Jira, your observability stack, and Slack or Teams? Can it coexist with IDE copilots — using them for editing while providing a shared context layer? What’s the realistic path for AI integration: can you start with one team and scale, or does it require a wholesale switch?

Best AI coding assistants for software engineers

Intelligent code assistance is where most teams start, and for good reason. These tools handle the mechanical parts of writing code — natural language code generation, code completions, scaffolding, simple refactors, test generation — and increasingly take on complex coding tasks like multi-file edits, code review, and codebase-wide refactors.

For most teams, coding assistants function as personal productivity tools for developers rather than the backbone of an organizational AI strategy. They're effective at producing functional code inside a single repo or workspace, but they typically have limited awareness of tickets, incidents, or design rationale; no unified view across multiple repos and services; and security and governance that varies by vendor and requires careful setup.

Here are the most used AI tools for code generation and editing:

Cursor

Cursor is an AI-native code editor built on Visual Studio Code that integrates AI-powered code completions, multi-file edits, and chat directly into the IDE. It can index your codebase for context-aware code suggestions and supports MCP for connecting to external tools and data sources. Cursor is especially popular with teams that want deep editor integration and codebase-wide awareness in a single environment. If your team wants a single environment where the AI sees the whole codebase, not just the open file, Cursor is the strongest option right now.

Key features: codebase indexing, multi-file editing, inline AI chat, tab completions, MCP support, agent mode for multi-step tasks.

GitHub Copilot

GitHub Copilot is the most widely adopted AI coding assistant, combining intelligent code generation, chat, and code review features available as a code extension in VS Code, JetBrains, and other editors. It’s tightly integrated with the GitHub ecosystem and supports agent mode for multi-step tasks. For teams already deep in the GitHub ecosystem, Copilot is the path of least resistance — it's everywhere, it's familiar, and the agent mode is catching up fast.

Key features: inline code suggestions, Copilot Chat, pull request summaries, code review suggestions, agent mode, broad editor support.

Claude Code

Claude Code from Anthropic is a command-line AI coding agent that operates directly in a developer’s terminal. Claude Code can read and edit multiple files, generate code snippets, run commands, search codebases, and manage git workflows. It supports Model Context Protocol (MCP), making it extensible to external data sources and tooling. It's the pick for engineers who prefer the terminal over a GUI and want agentic control without switching editors.

Key features: agentic terminal coding, codebase-wide edits, git integration, MCP integration, multi-step task execution.

Windsurf

Windsurf (now part of Cognition AI, originally built by Codeium) is an AI-native code editor that combines deep context awareness with inline completions, multi-file edits, and an agentic "Cascade" flow that can plan and execute complex coding tasks. It’s designed to understand your full codebase and keep history across sessions. (Note: Windsurf changed hands in mid-2025. Its original founders joined Google DeepMind, and Cognition AI separately acquired the product, brand, and remaining team. The product remains active, but the roadmap is evolving under new ownership.)

Key features: Cascade agentic workflow, codebase-wide context, session memory, real-time code suggestions.

Sourcegraph Cody

Cody brings AI-assisted coding to enterprise teams that need cross-repository context at scale. It uses Sourcegraph’s code graph to search and understand code across large, polyglot codebases, delivering context-aware suggestions that respect coding style and conventions across repos and services. (Note: Cody’s free and pro tiers were discontinued in July 2025; it is now available as an enterprise product.)

Key features: cross-repo code search and context, multi-editor support, enterprise code graph, inline completions and AI chat.

Best AI tools for engineering context and knowledge

This is where the engineering AI stack starts to differentiate — and also where most “best AI tools” roundups come up short.

Engineers are increasingly blocked not by writing code, but by chasing project context across GitHub, Jira, incident tools, wikis, logs, and Slack. In the 2025 JetBrains Developer Ecosystem Survey, 62% of developers said non-technical factors — collaboration, communication, and clarity — are as critical to their performance as technical ones. An AI coding assistant that can’t see those systems solves only part of the problem.

Enterprise context and knowledge platforms sit above individual tools. They connect the artifacts and people behind the code — not just the code itself — and answer questions like: What broke the last time this alert fired? Where’s the design doc for this service? Who owns this endpoint now?

These platforms unify code, tickets, logs, docs, designs, incidents, and discussions from tools like GitHub, Jira, observability tools, wikis, document stores, and Slack or Teams into a single, permission-aware, up-to-date context layer. They build links between artifacts — PRs ↔ tickets ↔ incidents ↔ designs ↔ chat — so teams can trace from an alert or question to what changed, who owns it, and relevant prior work.

Glean

Glean provides the context layer of the engineering AI stack. It builds an enterprise context graph over your real stack by connecting to code hosts like GitHub, GitLab, and Bitbucket; work tracking in Jira; incident and observability tools like PagerDuty and Datadog; docs and wikis in Confluence, Notion, and Drive or SharePoint; and chat in Slack and Teams.

For engineers, search becomes the entry point to a system. Using natural language descriptions of what they need — like "show me the main docs, tickets, incidents, and owners for the payments service" — they get results built from code, tickets, incidents, and ownership, not just keyword matches. Glean agents can turn Jira bug fixes or Slack threads into scoped implementation PRs, and engineers debugging in Cursor or Claude Code can call Glean via MCP to pull in recent incidents, tickets, ownership, and design docs without leaving the editor.

Key features:

• Enterprise context graph linking code, tickets, incidents, docs, and ownership
• Hybrid lexical + semantic search tuned to your codebase and jargon
• Permission-aware access inherited from source systems, enforced end-to-end
• MCP server for native integration with IDE coding assistants
• Agents that convert tickets and threads into scoped PRs and implementation plans
• Single-tenant, customer-controlled deployment on AWS, Azure, or GCP

Reported results: Teams using Glean’s context layer have reported measurable gains across the SDLC, including 20% faster time to ship code at Uber and $2.4M in annual engineering savings at LinkedIn from a Glean-powered Threat-bot agent. Two of the top three coding-assistant organizations rely on Glean to power the context layer behind their engineering tools.

Best AI tools for incidents, debugging, and observability

During an incident, engineers are usually juggling alerts, dashboards, logs, runbooks, Jira, and Slack while trying to answer three basic questions: what changed, what's broken, and who needs to be involved? The context-switching alone slows everything down. 79% of engineering teams are already exploring AI for incident tracking tasks.

AIOps, observability, and incident assistants typically live inside monitoring and incident response platforms, where they improve response times and resolution rates. They're strong at summarizing alerts, traces, and logs into human-readable narratives, highlighting likely regressions or components involved in an incident, and surfacing known fixes for recurring patterns.

But they’re typically scoped to a single data source — the metrics, logs, and traces for that specific tool. They don't see Jira workflows, code ownership, or design decisions stored elsewhere. For questions that span systems — like "what changed, who owns it, and what related incidents have we seen?" — they need a broader context layer to deliver a useful answer.

PagerDuty

PagerDuty’s AI capabilities focus on intelligent alert grouping, noise reduction, and automated incident triage. Its AI assistant can summarize ongoing incidents, recommend responders based on service ownership, and correlate alerts across services.

Key features: intelligent alert grouping, AI-powered triage, responder recommendations, automated diagnostics, integrations with Slack and Jira.

Datadog

Datadog offers AI-powered observability features including Watchdog, which automatically detects anomalies across metrics, traces, and logs. Its AI assistant helps engineers query observability data using natural language prompts and surface correlated root causes.

Key features: Watchdog anomaly detection, natural language querying, cross-service trace analysis, log pattern clustering, real-time dashboards.

ServiceNow

ServiceNow’s AI capabilities for IT operations include predictive intelligence for incident classification, automated assignment, and change risk assessment. Its Now Assist features bring generative AI to incident management, problem resolution, and change workflows.

Key features: predictive intelligence for incident routing, automated incident summaries, change risk scoring, knowledge article recommendations.

These tools are most effective when they can call into a broader context layer rather than operating in isolation. An incident assistant that can pull in related tickets, recent code changes, past incidents, and ownership from a unified context graph becomes far more useful. It can draft first-pass incident timelines, postmortems, and customer updates grounded in real events across your systems, not just the signals visible to one tool.

Best AI platforms and agent frameworks for engineering teams

General-purpose AI platforms and agent frameworks give engineering teams a place to centralize, monitor, and manage advanced AI capabilities across the organization. They include hosted LLMs, model hubs, agent-building frameworks, orchestration runtimes, and low-code or no-code builders for internal assistants and workflows.

These platforms are evolving rapidly, making it easier than ever to prototype assistants or agents and wire them to external tools. But by themselves, they don't provide an enterprise engineering graph; deep integrations with your code, tickets, incidents, and docs; or a consistent, permission-aware view across systems. They rely solely on whatever data and structure you feed them.

OpenAI (ChatGPT Enterprise / API)

OpenAI provides access to GPT models through ChatGPT Enterprise and its API platform. Teams use it for general-purpose reasoning, code generation, document analysis, and building custom assistants. ChatGPT Enterprise adds workspace management, SSO, and data privacy controls.

Key features: GPT model access, custom GPTs, function calling, data analysis, enterprise admin controls, API platform.

Anthropic Console + Claude with MCP

Anthropic’s Claude models, accessed via the Anthropic Console or API, are widely used for coding, analysis, and reasoning tasks. Claude’s native MCP support means teams can plug enterprise data and tools directly into AI-powered workflows.

Key features: Claude model access, MCP integration, large context windows, tool use, system prompts, API and console access.

Microsoft Foundry (formerly Azure AI Studio)

Microsoft Foundry provides a unified environment for building, evaluating, and deploying AI applications using models from OpenAI, Meta, Mistral, and others. It integrates tightly with Azure's security and compliance infrastructure and supports the Azure AI Agent Service for building and orchestrating autonomous agents.

Key features: multi-model catalog, agent service, prompt flow orchestration, evaluation tools, Azure security and compliance, deployment management.

Google Gemini Enterprise Agent Platform (formerly Vertex AI)

Google's AI platform, recently rebranded from Vertex AI, unifies agent building, scaling, governance, and optimization. It includes Agent Studio for low-code building, the Agent Development Kit (ADK) for code-first workflows, Agent Runtime for long-running agents, and Model Garden for access to 200+ models including Gemini and Claude.

Key features: Agent Studio, ADK for custom agents, Agent Runtime with persistent state, Model Garden, agent governance and security.

Amazon Bedrock / Agents for Bedrock

Amazon Bedrock offers access to foundation models from Anthropic, Meta, Mistral, and others, plus Agents for Bedrock to build and orchestrate multi-step agent workflows using AWS infrastructure.

Key features: multi-model access, Agents for Bedrock orchestration, knowledge bases, guardrails, AWS security and VPC integration.

For each of these platforms, the quality of output depends on what data and context you provide. A platform wired into a permission-aware enterprise context graph will produce better results than one operating on raw documents or API calls alone.

How to build the right AI stack for your engineering team

Most engineering teams assemble a portfolio of development tools. Coding assistants handle local editing and generation inside the IDE. Context platforms connect the dots across systems — code, tickets, incidents, docs, and people. Observability solutions provide the raw signals. Agent frameworks offer orchestration runtimes. The goal is making them work together rather than adding more tabs.

A two-layer model is emerging as the practical architecture:

Layer 1 — Enterprise context. This layer connects to code hosts, Jira, incident tools, observability tools, wikis, document stores, and Slack or Teams. It indexes everything with hybrid search (lexical + semantic), builds an enterprise graph linking services, APIs, incidents, tickets, owners, and design artifacts, and enforces security and governance boundaries end-to-end.

Layer 2 — Coding and interaction surfaces. This is where engineers handle daily coding tasks — IDEs, code hosts, work tracking, and collaboration tools. These surfaces are powerful, but they can't tap into enterprise context unless that context is supplied and structured for every query. 

Glean is built for Layer 1. It provides the shared, trusted view of your engineering environment that IDEs, incident consoles, agents, and workflows can plug into. Because it’s model-agnostic and exposes data through APIs and MCP-compatible tooling, multiple assistants and frameworks can call into the same graph rather than rebuilding their own embeddings and permissions logic. You index once; you reuse that context across tools and surfaces.

Three priorities for choosing AI powered development tools

1. Measure impact on your development process in terms of cycle time and quality, not adoption counts. The real gains show up in reduced rework, faster code review and incident resolution, and shorter onboarding ramps — not how many developers have a coding assistant installed.
2. Invest in reducing handoffs and rework across systems, not just faster code generation. 62% of developers say context reconstruction is what slows them down. A coding assistant alone doesn’t fix that.
3. Prioritize security, governance, and transparency before scaling. Where AI runs, what it can see, how it's audited, and whether engineers can verify why it gave a particular answer will matter as much as raw model quality. Trust and safety are prerequisites, not afterthoughts.

Get the full framework

Want to go deeper on the two-layer model? The software engineer's field guide to the AI stack breaks down the emerging tooling landscape, details how to implement the two-layer model in your environment, and walks through what engineering leaders should evaluate before scaling AI across the SDLC.

Frequently asked questions

How should engineering teams evaluate AI tools beyond demo performance?

Start with real workflows, not staged scenarios. The most reliable evaluation criteria are: what context the tool can actually see (repo-only vs. cross-system), how trust is built through grounding and citations, where your data goes and who can act on it, and whether the tool fits into the applications your team already uses — GitHub, Jira, Slack, your IDE. If a tool can't answer these cleanly in a pilot with your real data, it won't perform in production.

Can code generation assistants and context platforms work together?

Yes, and this is the direction most engineering teams are headed. Coding generation assistants like Cursor, Copilot, and Claude Code handle local editing and generation inside the IDE. A context platform like Glean provides the governed, organization-wide context layer those assistants can call via MCP from the editor. In practice, that means debugging a production issue in Cursor while pulling in related incidents, tickets, ownership, and design docs from Glean — without leaving the IDE.

What is the two-layer model for engineering AI?

The two-layer model separates the engineering AI stack into a context layer that unifies code, tickets, incidents, docs, and people into one governed graph, and coding and interaction surfaces where engineers do the work — IDEs, code hosts, Jira, and Slack. The context layer feeds the coding surfaces with real, trusted information so AI suggestions are grounded in your organization's actual systems, constraints, and ownership — not just patterns the model has seen before. For example, an engineer debugging in Cursor can pull in the relevant incident history, the design doc, and the service owner from the context layer without leaving the editor. That's the two layers working together.

How does Glean work with AI-powered coding assistants?

Glean sits in the context layer. It connects to code hosts, work tracking, incident tools, observability tools, wikis, and chat to build an enterprise context graph. Engineers access that context through search, Assistant, agents, and an MCP server that coding assistants like Cursor and Claude Code can call directly. Glean is single-tenant and customer-controlled, with permissions inherited from source systems and enforced end-to-end.

How long does it take to set up Glean for engineering workflows?

Teams can connect their main engineering tools — GitHub, Jira, Confluence, Slack, PagerDuty, Datadog, and others — and start using search, Assistant, and agents within days. Indexing begins immediately after authentication, and the enterprise context graph builds automatically as data is processed. Most teams start with one team or workflow and scale across the organization without re-implementing permission models in every tool.

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