# AI agents Essays and notes tagged AI agents. - [The AI-native SDLC: when typing code stops being the bottleneck](https://muthub.org/writing/the-ai-native-sdlc.md) (Jul 28, 2026): AI has made writing code cheap. Verification, trusted context and human judgment are now the scarce resources, and teams that redesign their lifecycle around them will pull away from teams that only add assistants. - [Context fragmentation: why your agents keep starting from zero](https://muthub.org/writing/context-fragmentation.md) (Jun 30, 2026): The knowledge an agent needs is scattered across code, tickets, chat, wikis, incident tools and people's heads. Each fragment is accurate and useless alone. Fragmentation, more than model quality, is what makes enterprise agents brittle. - [Model is a tool, harness is the strategy](https://muthub.org/writing/model-is-a-tool-harness-is-the-strategy.md) (May 26, 2026): Handing an AI agent a production codebase without a harness is like putting a teenager in a sports car with no dual brakes. Deterministic guardrails and sensors, owned as a platform, are what make agents safe to scale. - [Architecture that runs: why AI-era governance must be code](https://muthub.org/writing/architecture-that-runs.md) (May 12, 2026): AI now writes code faster than any review board can read it, while architecture still moves at meeting speed. Governance has to become something that executes in the pipeline and in the agent's context. - [Three architectures for AI coding agents, and how enterprises should choose](https://muthub.org/writing/three-architectures-for-ai-coding-agents.md) (Dec 16, 2025): Platform-embedded, sandboxed and evolutionary coding agents each earn trust in a different way. Then the editor became a fleet manager. Choose and govern agents by how their work will be verified. - [Agent-first data architecture: five principles](https://muthub.org/writing/agent-first-data-architecture.md) (Sep 30, 2025): Data platforms built for people asking questions will strain when the main consumer is software taking actions. Five principles for designing data platforms where agents are first-class users, and why an open lakehouse helps. - [Python or the JVM for production enterprise AI? Put the agent where the transaction lives](https://muthub.org/writing/python-or-jvm-for-enterprise-ai.md) (Jun 24, 2025): Python is unrivaled for research and prototyping. Production enterprise AI is mostly integration, security and scale, and with JVM agent frameworks now arriving, the runtime that owns the transaction is often the right home for the agent. - [Securing AI agents: identity, not secrets](https://muthub.org/writing/securing-ai-agents-identity-not-secrets.md) (May 29, 2025): Agents act on their own, at machine speed, across many systems. Static credentials and perimeter thinking don't hold up. Attested identity, explicit delegation and policy enforced outside the model do. - [From pair programming to fleet management](https://muthub.org/writing/from-pair-programming-to-fleet-management.md) (Dec 15, 2025): Editors now run local, background and cloud agents side by side. The leadership job shifts from writing code to designing the system that writes it. - [Agent design is still hard](https://muthub.org/writing/agent-design-is-still-hard.md) (Nov 25, 2025): High-level SDKs break under real tool use. Caching, reinforcement, failure isolation and shared state decide whether an agent is reliable. - [Rethinking operational databases for the agent era](https://muthub.org/writing/rethinking-oltp-for-agents.md) (Sep 10, 2025): The lakehouse unified analytics and AI. Bringing operational Postgres into it points toward one governed source of truth for agents. - [Beyond vibe coding: specs as executable artifacts](https://muthub.org/writing/spec-kit-beyond-vibe-coding.md) (Sep 8, 2025): Ad-hoc prompting yields code that looks right but misses intent. Making the spec the gated source of truth is how AI-assisted delivery becomes repeatable. - [Closing the loop: agents that verify their own work](https://muthub.org/writing/closing-the-loop.md) (Aug 13, 2025): An agent that runs the tests, reads the failure and fixes its own work is the line between autonomy and a one-shot tool. - [A data engineering agent in practice](https://muthub.org/writing/bigquery-data-engineering-agent.md) (Jul 2, 2025): An experimental agent built pipelines, changed schemas, reverted its own mistakes and generated quality checks from plain language. Self-correction was the part that mattered. - [Remote MCP, straight from the API](https://muthub.org/writing/mcp-connector-messages-api.md) (Jun 4, 2025): Direct remote MCP access through the Messages API removed a client layer and moved agent integration from clever hacks toward engineering. - [A2A and MCP: complementary, not competing](https://muthub.org/writing/a2a-and-mcp.md) (May 1, 2025): MCP connects agents to tools; Agent2Agent connects agents to each other. Together they turn single agents into systems, and raise the governance stakes. --- Muthukrishnan (Muthu) Bommiah · https://muthub.org · Views are my own and do not represent my employer.