AI Agents
Everything related to agentic flows, harnesses, development with agents, building agents.
Bind agent outputs to their bytes, record how each was produced, and verify the digest, builder, and inputs against policy before use.
How to bound agent work at admission, track queue age, choose overflow behavior, preserve fairness, and return useful overload responses.
Separate caller cancellation from deadlines, propagate both, and reserve part of every agent run budget for cleanup and reporting.
Set separate retention and deletion rules for agent conversations, traces, tool payloads, embeddings, caches, and evaluation data.
Compare developer machines, team-operated servers, and managed services by state, identity, isolation, cost, reproducibility, and blast radius.
Limit agent reads, isolate enforcement, treat build-file writes as execution, gate deletion, and pair disk controls with network egress policy.
How to preserve intent, prevent duplicate work, trace execution, and recover failures when agents hand tasks to other systems.
A distinct identity preserves agent audit attribution, narrows permissions and allows revocation without disabling the person who launched the run.
Set agent release gates that catch slice-level quality losses, account for uncertainty, and cap cost, latency, turns, and tool calls.
Use expand–migrate–contract, compatible readers, and idempotent initialization to change persisted agent state without breaking resumed runs.
Choose planning depth by uncertainty, subtask independence, review needs, re-planning cost, and how difficult each action is to undo.
Treat tool names and parameter schemas as agent-facing APIs: classify breaking changes, deploy versions in parallel, and retain per-version traces.
Restore agent sessions after WebSocket loss with stable session IDs, acknowledged event cursors, and ordered replay without UI gaps or duplicates.
A practical method for capturing agent tasks, state, tools, production failures, labels, privacy review, and a prompt-tuning holdout.
Release model, prompt, tool, and policy changes to a sticky cohort, measure the result, and roll back without stranding side effects.
Bind feedback to complete agent runs, separate correctness from presentation, and turn human corrections into reusable evaluation targets.
Compare plain loops, framework harnesses, and managed runtimes by failure recovery, tool portability, prompt control, tracing, and upgrades.
Stop failing tools from consuming agent time and worsening outages by adding scoped breakers, bounded retries, recovery intervals, and limited probes.
How to record reversible agent actions, mark the point of no return, and make compensation idempotent, resumable, and operable.
A context policy for long agent runs: trim tools, admit relevant evidence, persist editable conclusions, compact carefully, and test drift.
Carry trace and business operation IDs through agent runs, model calls, tools, and queues without leaking customer data.
Choose cron, event-driven triggers, or both by weighing execution frequency, detection delay, duplicate delivery, bursts, and repair.
How to quarantine repeatedly failing agent jobs, preserve evidence for diagnosis, and replay them only after the failure condition changes.
Pin the whole agent runtime, verify provenance, and route dependency updates through a tested release lane the running agent cannot bypass.