The Five-Agent Research Pattern: Surveying a New LLM Provider Before You Tune It

The Five-Agent Research Pattern#

Adopting a new LLM provider for a coding-agent role looks easy from the docs. Read the model card, copy the partner adapter’s defaults, ship. A week later you find out the provider rejects tool_choice=required in thinking mode, the docs lied about reasoning_content echoing, and your retry loop multiplies the per-turn timeout by 3x because the rate-limit response isn’t JSON.

The docs miss what was patched after release. The community catches what the docs miss. Partner adapters encode lived defaults nobody published. Your own adapter has bugs you can’t see from inside it. Reading any one of these in isolation gets you to “I think I understand this provider.” Reading all five in parallel gets you a knob list, an open-contradictions list, and a list of bugs to fix before the matrix runs. The pattern: spawn 5 parallel research sub-agents, one per angle, then synthesize.

Agent Context Preservation for Long-Running Workflows: Checkpoints, Sub-Agent Delegation, and Avoiding Context Pollution

Agent Context Preservation for Long-Running Workflows#

The context window is the single most important constraint in agent-driven work. A single-turn task uses a fraction of it. A multi-hour project fills it, overflows it, and degrades the agent’s reasoning quality long before the task is complete. Agents that work effectively on ambitious projects are not smarter – they manage context better.

This article covers practical, battle-tested patterns for preserving context across long sessions, delegating to sub-agents without losing coherence, and avoiding context pollution – the gradual degradation that happens when irrelevant information accumulates in the working context.

Long-Running Workflow Orchestration: State Machines, Checkpointing, and Resumable Multi-Agent Execution

Long-Running Workflow Orchestration#

Most agent examples show single-turn or single-session tasks: answer a question, write a function, debug an error. Real projects are different. Building a feature, migrating a database, setting up a monitoring stack – these take hours, span multiple sessions, involve parallel work streams, and must survive context window resets, session timeouts, and partial failures.

This article covers the architecture for workflows that last hours or days: how to model progress as a state machine, how to checkpoint for reliable resumption, how to delegate to parallel sub-agents without losing coherence, and how to recover when things fail partway through.